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PeskAAS: A near-real-time, open-source monitoring and analytics system for small-scale fisheries

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Small-scale fisheries are responsible for landing half of the world’s fish catch, yet there are very sparse data on these fishing activities and associated fisheries production in time and space. Fisheries-dependent data underpin scientific guidance of management and conservation of fisheries systems, but it is inherently difficult to generate robust and comprehensive data for small-scale fisheries, particularly given their dispersed and diverse nature. In tackling this challenge, we use open source software components including the Shiny R package to build PeskAAS ; an adaptable and scalable digital application that enables the collation, classification, analysis and visualisation of small-scale fisheries catch and effort data. We piloted and refined this system in Timor-Leste; a small island developing nation. The features that make PeskAAS fit for purpose are that it is: (i) fully open-source and free to use (ii) component-based, flexible and able to integrate vessel tracking data with catch records; (iii) able to perform spatial and temporal filtering of fishing productivity by fishing method and habitat; (iv) integrated with species-specific length-weight parameters from FishBase ; (v) controlled through a click-button dashboard, that was co-designed with fisheries scientists and government managers, that enables easy to read data summaries and interpretation of context-specific fisheries data. With limited training and code adaptation, the PeskAAS workflow has been used as a framework on which to build and adapt systematic, standardised data collection for small-scale fisheries in other contexts. Automated analytics of these data can provide fishers, managers and researchers with insights into a fisher’s experience of fishing efforts, fisheries status, catch rates, economic efficiency and geographic preferences and limits that can potentially guide management and livelihood investments.
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RESEARCH ARTICLE
PeskAAS: A near-real-time, open-source
monitoring and analytics system for small-
scale fisheries
Alexander TilleyID
1
*, Joctan Dos Reis LopesID
1
, Shaun P. Wilkinson
2,3
1WorldFish, Bayan Lepas, Penang, Malaysia, 2School of Biological Sciences, Victoria University of
Wellington, Wellington, New Zealand, 3Wilderlab, Wellington, New Zealand
These authors contributed equally to this work.
*alex.tilley@gmail.com
Abstract
Small-scale fisheries are responsible for landing half of the world’s fish catch, yet there are
very sparse data on these fishing activities and associated fisheries production in time and
space. Fisheries-dependent data underpin scientific guidance of management and conser-
vation of fisheries systems, but it is inherently difficult to generate robust and comprehensive
data for small-scale fisheries, particularly given their dispersed and diverse nature. In tack-
ling this challenge, we use open source software components including the Shiny R pack-
age to build PeskAAS; an adaptable and scalable digital application that enables the
collation, classification, analysis and visualisation of small-scale fisheries catch and effort
data. We piloted and refined this system in Timor-Leste; a small island developing nation.
The features that make PeskAAS fit for purpose are that it is: (i) fully open-source and free
to use (ii) component-based, flexible and able to integrate vessel tracking data with catch
records; (iii) able to perform spatial and temporal filtering of fishing productivity by fishing
method and habitat; (iv) integrated with species-specific length-weight parameters from
FishBase; (v) controlled through a click-button dashboard, that was co-designed with fisher-
ies scientists and government managers, that enables easy to read data summaries and
interpretation of context-specific fisheries data. With limited training and code adaptation,
the PeskAAS workflow has been used as a framework on which to build and adapt system-
atic, standardised data collection for small-scale fisheries in other contexts. Automated
analytics of these data can provide fishers, managers and researchers with insights into a
fisher’s experience of fishing efforts, fisheries status, catch rates, economic efficiency and
geographic preferences and limits that can potentially guide management and livelihood
investments.
Introduction
Approximately half of the global catch of fish is landed in small-scale fisheries (SSF) [1], yet
the ability for science to guide the sustainable exploitation of these resources is inhibited by
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OPEN ACCESS
Citation: Tilley A, Dos Reis Lopes J, Wilkinson SP
(2020) PeskAAS: A near-real-time, open-source
monitoring and analytics system for small-scale
fisheries. PLoS ONE 15(11): e0234760. https://doi.
org/10.1371/journal.pone.0234760
Editor: Ismael Aaron Kimirei, Tanzania Fisheries
Research Institute, UNITED REPUBLIC OF
TANZANIA
Received: June 15, 2020
Accepted: October 26, 2020
Published: November 13, 2020
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0234760
Copyright: ©2020 Tilley et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The peskAAS catch
questionnaire, R script and data used to test the
application in this study are archived in Dataverse
https://doi.org/10.7910/DVN/TVGIJJ. The peskAAS
substantial data gaps. There are 40 million people actively fishing in inland and coastal envi-
ronments worldwide, yet due to the challenges involved in collecting data in dispersed, infor-
mal and diverse fisheries, the contributions of SSF to livelihoods, and food and nutrition
security, are frequently hidden and poorly accounted for [2]. As a result, fisheries solutions
are underrepresented in investments and policies to address food and nutrition security [3].
Recent research has shown that the nutrient qualities and quantities of fish already caught
would be sufficient to address major micronutrient deficiencies for many countries [4], and
that fish-based food strategies hold untapped potential to address nutrition short falls.
Refinements to the management of fisheries towards sustainability and food and nutrition
security goals can be supported, amongst other things, by higher quality and more comprehen-
sive fisheries and value chain data. Addressing these gaps with small-scale fisheries in low-
income countries requires sensitivity to the limits of resources and capacity to collect, store,
analyse and respond to these data [5]. Even with adequate data, much fisheries science relies on
statistical models and analytics that were developed for single species fisheries, rather than biodi-
verse tropical fisheries where multiple species frequently comprise the harvests from diverse and
biodiverse habitats [6]. Many diagnostic and statistical tools have been developed to support fish-
eries decision-making or stock assessment in data-poor contexts, e.g., FishPath [7] or TropFishR
[8]. However, very few solutions provide end-to-end integration from data collection at the
point of fishing or landing, to visualisation of data summaries for managers and fishing commu-
nities. This leads to a situation where management and policies are built on poor government
estimates or reconstructed trade and consumption statistics that are often not relevant at the
local scale, and hence are highly susceptible to governance and management failures [9].
Over the past two decades, the proliferation of information and communication technologies
(ICTs) has revolutionised the collection, communication and storage of data in food production
sectors, including in the industrial fishing sector [10]. These ICTs innovations commonly
include design and development of mobile smartphone applications and digital survey forms
[11]. There are some examples of applications and tools to collect fisheries data on a small scale,
the most successful of which are generally ‘high touch’, meaning they involve significant contex-
tual development that cannot easily be scaled to other systems or geographies (see [1215]). The
danger in developing scalable technologies is that they are often imposed as prescribed ‘solu-
tions’ on low-income countries, and can merely reinforce the capacity gap, alienate managers
and stakeholders, and be ill-suited to the contextual reality [16,17]. There is an urgent need for
a light touch, scalable and integrated approach to data collection and analytics, but thus far, this
has been elusive in terms of getting simple, usable data in the hands of fisheries managers.
Here, we describe a new digital application called PeskAAS—a pseudo-acronym for fisher-
ies (peskas) in the national language of Timor-Leste, Tetum, combined with Automated Ana-
lytics System. PeskAAS connects open source programs to collect, communicate, analyse and
visualise SSF movement and catch data. We piloted PeskAAS to determine how scripted ana-
lytics can assist fisheries management by generating digestible, summarised information rap-
idly for decision-making in data-deficient and low capacity systems. We provide examples to
illustrate the types of analytics and machine learning that the system can be used to carry out.
Our test site of Timor-Leste was chosen due to the very limited level of fisheries development,
the paucity of information, and the limited fisheries governance capacity.
PeskAAS overview
PeskAAS is an interactive web-hosted R Shiny application developed to facilitate data explo-
ration and decision-making processes in small-scale fisheries. This application accesses the
‘peskaDAT’ database in real-time using the DBI and RMySQL R packages [18,19], and pulls
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code and documentation are hosted at https://
github.com/shaunpwilkinson/peskAAS.
Funding: This work was undertaken as part of the
CGIAR Research Program on Fish Agri-Food
Systems (FISH) led by WorldFish, and the CGIAR
Big Data Platform Inspire Challenge 2018 led by
CIAT and IFPRI. These programs are supported by
contributors to the CGIAR Trust Fund. PeskAAS
was established under the Fisheries Sector Support
Program funded by the Royal Norwegian Embassy
in Jakarta. The funders provided support in the
form of salary for authors [A.T.] and field costs, but
did not have any additional role in the study design,
data collection and analysis, decision to publish, or
preparation of the manuscript. Wilderlab is a
recent, unassociated affiliation for S.P.W. and did
not contribute funding towards the study. The
specific roles of these authors are articulated in the
‘author contributions’ section.
Competing interests: The authors declare there are
no competing interests. SPW’s affiliation to
Wilderlab does not alter our adherence to PLOS
ONE policies on sharing data and materials.
catch records collected by enumerators at landing sites into a web-based interactive R session
(hosted by https://www.shinyapps.io). From this dashboard, users can apply dynamic location,
gear, habitat and date-range filters to create informative plots for catch per unit effort (CPUE),
total catch, species composition and total national catch estimates (Fig 1).
The user interface incorporates tick-box widgets for dynamic selection of municipalities,
fishing habitats, gear types and boat type, and a slider widget is included for users to fit cubic
smoothing splines weighted by trip effort to the CPUE data with a user defined smoothing
parameter that is dynamically adjustable to aid in visualization. Users also have an option
to download a month-aggregated summary table in .csv format for further analysis, using a
download-button widget.
The PeskAAS application is hosted remotely by shinyapps.io (https://www.shinyapps.io/)
on the free pricing tier, which currently allows 25 active hours of use per month. Users can
also run the application locally, by cloning the repository from GitHub and launching the
application from a local R session, or by installing RStudio Shiny Server (https://www.rstudio.
com/products/shiny/shiny-server/) and hosting the application on a local machine.
The PeskAAS application consists of a network of freely available components for data
collection, storage, manipulation and analysis, catering to humanitarian organizations with
low usage requirements (<25 active hours per month) (Fig 2). The application is scalable for
higher usage levels with modest subscription levies for KoBo toolbox, Shiny (https://www.
shinyapps.io), Heliohost (https://www.heliohost.org) and Google Cloud Platform (GCP;
https://cloud.google.com).
Methods
We developed and piloted PeskAAS in Timor-Leste from 2016 to 2019. In May 2019, the
Timorese Government adopted and launched this technology as their official national fisheries
monitoring system.
Catch documentation at landing sites
Fisheries catch data were collected on 3G-enabled android tablets using a digital survey form
developed in KoBo toolbox (http://www.kobotoolbox.org/) a free suite of tools for field data
Fig 1. Screenshot from the interactive PeskAAS dashboard developed to visualise near-real time fish landings
data in Timor-Leste. https://worldfish.shinyapps.io/PeskAAS/.
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collection. One data enumerator was hired in each of the 11 coastal districts to cover 25 land-
ing sites across Timor-Leste (Fig 3). Atauro Island to the north of Dili (the national adminis-
trative centre) falls within the municipality of Dili, and is a disproportionately important area
for national fisheries with a high population of fishers, so three enumerators are responsible to
record landings across 12 small landing sites. Enumerators interview individual fishers (i.e.,
the names of fishers are collected, and their catch profile directly associated to them) when
they return from fishing. Data collected include transport type (motor, canoe, on foot), gear
type (gill net, seine net, beach seine, hand line, long line, spearfishing, trap, gleaning), habitat
type where fishing took place (reef, fish aggregating device, deep, shore or mangrove), number
of fishers (i.e., number of men, women and children engaged in that fishing trip), trip duration
(rounded to nearest hour), species or species group, sizes (fork length to nearest cm) of all fish
landed and number caught by species or species group, and the sale price per kg or per number
of fish, if the catch was destined for market. The catch survey form evolved through time to
integrate a growing list of fishers and identified species, and in response to various feedback
mechanisms, such as error flagging and workshops with fisheries managers. Species of
Fig 2. A diagrammatic representation of the PeskAAS application. From bottom left, the catch from a fishing vessel is entered into a KoboCollect
survey form on a smartphone. These data are uploaded to the KoboToolbox database (DB). The PesksPARSE.R script pulls these data along with the
movement track of the fishing trip from PDS via an application-programming interface (API) every 24 hours. These data are then checked and filtered, and
uploaded to the PeskaDAT database. The PeskAAS Shiny web app queries PeskaDAT and displays near-real-time graphics and analytics.
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Fig 3. Map of Timor-Leste illustrating landing sites where small-scale fisheries catch was recorded using
smartphones or tablets.
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particular local importance were selected based on index of relative importance analysis on
Timor-Leste fisheries [20] and these are retained at species level. All other species captured in
the fishery are classified by family, or aggregated further into one of the nine groups of species
constituting the Marine Fishes division of the FAO International Standard Statistical Classifi-
cation of Aquatic Animals and Plants [21] (S1 Data).
All research was authorised and conducted in partnership with the Timor-Leste Ministry of
Agriculture and Fisheries. All catch data collection was carried out with the verbal consent of
the fisher. All participation in catch data monitoring and vessel tracking was voluntary, and
fishers not willing to participate were not prejudiced against in any way.
peskaDAT database
We developed a cloud-based MySQL database of filtered landings records called ‘peskaDAT’,
to host cleaned and checked fisheries landings data, geo-located boat tracks and ancillary tables
such as species, boat, gear and habitat information in an easily accessible and mutable format
(S2 Data). This database is hosted by Heliohost, a free hosting platform with several MySQL
relational database servers. A monthly database backup is created and stored locally in.rds
format (an R binary data file) in a Dropbox folder to ensure data perpetuity and backwards
compatibility.
peskaPARSE.R script
To automatically access landing records from KoBo toolbox, and filter, manipulate and submit
the cleaned records to the peskaDAT database on a regular (daily) basis, we developed an R
script called ‘peskaPARSE.R’ (S3 Data) that is scheduled on a daily cron job (a command to a
server to be executed at a specified time) run on a Google Cloud Platform virtual machine.
The script first accesses the KoBo toolbox API (application programming interface that allows
access to the data) and pulls the new catch records into the R environment. The catch weight
for each new record is estimated using length-weight parameters obtained from FishBase
(www.fishbase.org) using the rfishbase R package [22]. Several filters are applied according to
thresholds including species- or group-specific length, number of individuals, weight and
price boundary checks, formatting error checks, and invalid gear/habitat combination checks.
Suspect entries are flagged for curation by a moderator, and their values are automatically sup-
pressed from contributing to downstream applications (including the PeskAAS dashboard in
Fig 1) until records are amended in the KoBo toolbox dashboard. This is achieved via prompt
follow-up clarification with field-based observers, to ascertain whether data entry errors were
made during form submission or whether the record in question is a genuine outlier.
To automatically run the peskaPARSE.R script on a daily basis, we deployed a free Ubuntu
18.04 minimal virtual machine (VM) instance on the Google Cloud Platform, transferred the
script to a convenient user directory (specified by the [address] argument below), and sched-
uled a 24-hourly cron job by appending the following line to the /etc/crontab file:
0 0    root Rscript -e 'source("[address]")'
where [address] is replaced by the web location of the R script. In our case we stored the
script in a publicly available Dropbox directory and modified the public link to terminate
with download = 1 to enable direct sourcing (see https://github.com/shaunpwilkinson/
PeskAAS for further details). For the script to successfully import the raw trip- and landing
records from the KoBo and Pelagic Data Systems APIs, and deposit the filtered records in the
peskaDAT MySQL database, three authorization files must be stored in the same directory
as the peskaPARSE.R file: a single-line text file named KoBo-auth.txt containing the KoBo
toolbox username and password, delimited by a colon operator, and similarly-formatted files
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containing log-on credentials for the Pelagic Data Systems dashboard (PDS-auth.txt) and the
peskaDAT MySQL database (peskaDB-auth.txt; user with SELECT privileges). The regular
cleaning and filtering of new landings records ensures that the data available for analysis and
visualization are timely and trustworthy.
Geospatial vessel tracking
To visualise geospatial fishing effort and extrapolate fishing effort from individual trips to
community, municipal and national levels, we installed tamperproof solar-powered GPS units
(PDS v1.25c) developed by Pelagic Data Systems Inc. (San Francisco, CA) on to 5–15 boats
per landing site (N = 437). Tracking was voluntary and fishers bore no costs. Trackers record
point location data automatically every 5 seconds and communicate those data when in range
of a cellular network (Fig 4). Vessel tracks were also linked to the trip’s catch data, where avail-
able, using the unique GPS unit ID, allowing us to train a model to predict unknown variables
for trips with GPS data only, such as gear and habitat type.
Fisheries analytics
To quantify how catch volume varied over time and space, the catch-per-unit-effort (CPUE) is
used as a metric for relative fish abundance, and to track the response of fish stocks to fishing
pressure [24]. For CPUE, a standard unit of effort is required. For PeskAAS, we use raw
CPUE, with effort standardized into the unit of fisher-hours on each fishing trip, calculated as
the trip duration in hours multiplied by the number of people who were actively fishing on
that boat during that trip. The complexity of more robust CPUE standardization (see [24]) of
multiple gear types in tropical, multi-habitat, mixed species fisheries makes it impractical in a
livelihoods context, so fisher hours is used as a unit applicable across all fisheries and scales.
To generate accurate regional and national catch and CPUE estimates, it is necessary to esti-
mate the frequency and duration of fishing trips for each boat type, known as a vessel activity
coefficient (VAC). To generate an accurate VAC accounting for differences in boat type, vessel
tracks were used to generate monthly VAC values according to motorized and non-motorized
Fig 4. The relative effort heat map of small-scale fishing vessel tracks between August 2018 and February 2020 in Timor-Leste from the
PeskAAS dashboard. Red shading represents areas of highest effort (near to homeports and shore), and orange to yellow shading shows areas fished
with decreasing effort further from shore. Map data ©OpenStreetMap contributors. Map layer by Esri via leaflet [23].
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vessels, and were used to calculate the first national scale estimates of fish production based on
SSF landings in Timor-Leste (Eq 1).
The national monthly catch (in tons; C) is estimated as:
C¼XbCPUEbEPTbVACbNb0:001 ð1Þ
where brefers to the boat type (canoe or motor-powered; shore based fishing accounted
for <2% of reported landings and was hence omitted from the analysis); CPUE is the monthly
catch per unit effort (averaged across all sites, in kg per fisher-hour); EPT is the median effort
per trip (fisher-hours); VAC is the vessel activity coefficient (average number of trips per
month); and N
b
is the total number of boats of type bin the national fleet (national fisher and
boat census data obtained from the Timor-Leste Ministry of Agriculture and Fisheries).
Supervised prediction of missing trip attributes
The integration of shore-based observer data with high-resolution geo-located vessel fishing
tracks from on-board GPS units from Pelagic Data Systems Inc. allowed us to validate a super-
vised classification approach for predicting gear and habitat types in the case where only GPS
data are available (i.e. trips that are tracked but catch is not recorded by enumerators). For
each tracked but unobserved trip, a donor trip is selected from the subset of trips for which
both GPS and observer data are available, based on spatial nearest-neighbour analysis [25]. To
achieve this, ten GPS vectors from each trip spaced at even time intervals are used to generate
training- and query-data matrices for input into the nn2 function in the RANN R package
[26]. Donor trips are assigned based on minimum Euclidean distance and are only assigned if
the distance is <0.20 km. To validate this approach, we randomly allocated the linked trip set
available as of 31 July to 80% training data and 20% query data, and tested the success rate in
terms of number of gear and habitat types correctly predicted in the query set over 100 itera-
tions. The nearest-neighbour classification approach correctly predicted 83.4% (SD = 0.023%)
of gear types and 91.9% (SD = 0.021%) of habitat types based on 741 training observations and
183 query observations.
Results & discussion
At the pilot’s inception in 2016, the Timor-Leste government and fishers had very sparse quan-
titative data with which to characterise fisheries or determine production trends over time.
PeskAAS was developed concurrently with the co-generation of a national database of SSF
landings with the Timor-Leste National Fisheries Directorate. As of 31 July 2020, 59,000 fish-
ing trips have been geotracked, representing 810,000 km travelled over 313,000 hours. Catch
data was recorded for 29,251 trips, resulting in 32,872 landings records representing a total
catch weight of 298.5 tonnes. PeskAAS has already been utilised to test the effectiveness of
fish aggregating devices at the boosting catch rates of nearshore artisanal fishers [27] and
enabled the first calculation of national fisheries production for the Timor-Leste SSF fleet
[20]. The full adoption of PeskAAS as the official national fisheries monitoring system of the
Timorese government in 2019 suggests legitimacy and the potential for sustainable fisheries on
a national level.
PeskAAS was designed to be a free, online data exploration tool to enable rapid develop-
ment of simple, near-real time fisheries monitoring systems for accurate and timely fish pro-
duction data to use in adaptive, evidence-based management of fisheries resources. The
tracking hardware and data service from Pelagic Data Systems (PDS) is an optional extra and
represents additional costs to governments that must be considered in terms of sustainability.
All the analytics of PeskAAS relating to CPUE are done without PDS data. We found effort
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estimations that fishers provided if asked were sufficiently accurate, and a VAC can be calcu-
lated from this by averaging all trip durations recorded from certain fishers. The specific values
of the vessel tracking functionality were 1) an understanding of effort and activity distribution
and, 2) the ability to calibrate effort through dynamic VAC calculation over time, and 3) in
certain contexts (not tested here) may provide a safety at sea function.
If collected and managed in inclusive processes with fishers, fishing location data can be
used by fishers to better argue and prove tenure and/or use rights (i.e., in line with the FAO
Voluntary Guidelines on fisheries [28] and tenure [29]), for example in marine spatial plan-
ning processes that can otherwise ignore complex socio-political inequalities [30]. This value
was realised in the case of the TS Taipei container ship grounding in Taiwan, where spatial
and catch records from small-scale fishers were utilised to leverage fair reparations to fishers
from the shipping company [14]. To date the spatial information obtained from combined ves-
sel tracking with PeskAAS has not been used for coastal zone management, but protecting
the rights of Timorese fishers to marine areas is a key commitment in the new draft National
Fisheries Strategy (2018) that the PeskAAS data and team helped to inform. In other contexts,
there are concerns that such data might work against fisher livelihoods, i.e. in nations and
regions where exclusive no take zones and top down compliance are preferred strategies [31,
32]. The intended and unintended implications of data innovations should be considered
from the outset of any such pilot and subsequent scaling out phases.
The co-design of PeskAAS with fisheries managers was crucial in building a financially and
institutionally sustainable system. This same user-centred design process ensured that the Pes-
kAAS analytical workflow was not initially too complex, and only included statistics and insights
that could be readily interpreted by fisheries management users. Hence, in scaling this tool to
other countries and contexts, the need to build local legitimacy and a feeling of ownership
amongst government stakeholders will necessitate a process of co-design. The technical capacity
in Timor-Leste for digital systems is improving, but as in most low-income country govern-
ments, the long-term maintenance of the system will still require external support. Ongoing
work on PeskAAS will develop its modularity and customisation through templates for catch
forms, analytics package integrations (such as TropFishR [8]) and dashboard tutorials to guide
managers, to allow for more flexibility to other fish production systems, countries and capacities.
PeskAAS was developed as an open source script to make it highly adaptable to different
fisheries contexts and strategic objectives. The catch form and scripted analytics require
no specialised equipment and the dashboard is displayed in a web browser. However, we
acknowledge that the scripted nature of PeskAAS limits its use to those with programming
expertise, or implies a need to hire help that may be uncommon or unavailable in low-income
countries. Rather than be an off-the-shelf solution, we envision PeskAAS to be a software
framework that external NGOs or multilateral agencies can utilise to establish a highly contex-
tualised, user-centred system for obtaining and using data from small-scale fisheries to guide
decision-making. Data generated by PeskAAS were used to show how fishing technology
could improve fisher incomes and fish availability in Timor-Leste [27], which substantiates
government investment in interventions that are more cognisant and sensitive to SSF. Future
work will focus on developing modules and templates that can be populated by local actors in
new fisheries contexts to reduce the coding workload and streamline implementation, but we
anticipate some level of technical assistance will continue to be required.
PeskAAS in Timor-Leste is open access and publicly available. In theory, anyone with
access to a computer, tablet or smartphone, and an internet connection, are able to see the
data aggregated to the municipal level. The reality is that in Timor-Leste, as in many low-
income countries, there are still substantial barriers (e.g., cost of technology, digital literacy,
formal identification) to digital inclusion (the access to and use of ICTs) that limit the extent
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to which fishers can access and benefit from such data. Addressing these limitations represents
challenges for future pilots where participatory action research principles would be relevant
[33] and where initiative objectives include empowering individual fishers in management
decision-making, or improving their livelihoods through fisher-level business planning. This
was beyond the scope of the pilot we describe here.
Further capacity building is also necessary to ensure PeskAAS outputs are being correctly
interpreted to inform policy and guide extension services delivered by government actors to
and for fishing communities in Timor-Leste. Better data systems can only drive informed deci-
sion-making if the information is available and understandable. Currently, this PeskAAS is
suited to boat-based fisheries, compounding the unawareness and exclusion of foot fisheries
often conducted by women [34]. To get a full picture of SSF and enable local legitimacy and
sustainability, Timor-Leste must build on initial successes with co-management [35] as a
platform on which to base a range of data collection methods and strategies. This will ensure
stakeholders are included in contributing local ecological knowledge and forming shared
understanding and strategies for stewardship of their aquatic resources.
In conclusion, the pilot of PeskAAS enabled system refinements, illuminated limitations
and developed preliminary data for decision makers. The data have been taken into consider-
ation in national-level fisheries governance, through the new draft National Fisheries Strategy
(2018) and revised Fisheries Decree Law (2019). The application has potential for government
and non-government managers, researchers and students looking for a low cost solution to
address SSF data gaps.
Supporting information
S1 Data. List of species, species groups and family categories used for recording catch
along with a and b parameters used to calculate weight from length measurements.
(DOCX)
S2 Data. MySQL relational database schema with cascading foreign keys shown by dashed
arrows. Figure generated in MySQL Workbench (Oracle Corp.).
(DOCX)
S3 Data. PeskaPARSE.R’ script file. The PeskaPARS.R script scheduled on a daily cron job,
run on a Google Cloud Platform virtual machine.
(R)
Acknowledgments
We are very grateful to the Timorese fishers and community members who collaborated with
us and Timor-Leste’s Ministry of Agriculture and Fisheries in this project. Particular thanks go
to: Joctan Dos Reis Lopes for leading the PeskAAS team on the ground; to the WorldFish
Timor-Leste team members Dave Mills, Mario Gomes, Mario Pereira and Agustinha Duarte
for providing important local insights throughout the co-design process; to Pedro Rodrigues,
Lucas Fernandes and the Fisheries Directorate staff for helping us to build from the ground
up; and to Pip Cohen for insight and comments on this manuscript.
Author Contributions
Conceptualization: Alexander Tilley.
Data curation: Alexander Tilley, Joctan Dos Reis Lopes, Shaun P. Wilkinson.
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Formal analysis: Alexander Tilley, Joctan Dos Reis Lopes.
Funding acquisition: Alexander Tilley.
Methodology: Alexander Tilley, Joctan Dos Reis Lopes, Shaun P. Wilkinson.
Project administration: Alexander Tilley.
Software: Shaun P. Wilkinson.
Supervision: Alexander Tilley.
Validation: Joctan Dos Reis Lopes.
Visualization: Alexander Tilley, Shaun P. Wilkinson.
Writing – original draft: Alexander Tilley, Shaun P. Wilkinson.
Writing – review & editing: Alexander Tilley, Shaun P. Wilkinson.
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... However, VMS and AIS monitoring systems have been implemented in industrial fleets, and in limited cases in SSF in developed countries (James et al., 2018). More recently, some interactive platforms that combine tracking data and catch data have been developed to support fishers and managers decisions (e.g., D'Andrea et al., 2020;Tilley et al., 2020). Unfortunately, some regions lack logistic or technical capabilities to monitor small-scale fleets over a wide range of fishing areas to obtain explicit spatial data (catch and effort). ...
... If well, we addressed only a part of the complexity associated with the SSF using the RF model to classify fishing and transit positions of vessels, it is still necessary to increase the number of trips with onboard observers to improve the analytical forecasting capacity of the approach used in this study. Lastly, it is important to indicate that from the pilot VMS data, we could not infer which species were caught or the fishing gear used; thus, incorporation of these data could help to analyze such relationships (fishing gears-target species) and add more detail to differentiate the potential fishing grounds according to different target species (e.g., D'Andrea et al., 2020;Tilley et al., 2020). Despite the limitations in our study, our results showed the usefulness of VMS for SSF to help understand the fleet dynamics and provided insights into the potential fishing grounds available in the SGoM region for the first time. ...
... Nowadays, the implementation of a VMS in the region is challenging, from gaining the acceptance of fishers to issue of the costs involved in the implementation of the system and its maintenance, especially given the size of the fleet (in the region there are around 20,000 small-scale vessels; Coronado et al., 2020). Thereby, a VMS for SSF demands the development of a cost-effective platform for daily catch recording in an integrative way, including the type of gears and species captured whenever possible (e.g., D'Andrea et al., 2020;Tilley et al., 2020). This type of data could be coupled with that obtained from onboard observers, and a program of monitoring systems in the coast, thus the acquisition of explicit spatial data from the smallscale vessels (Russo et al., 2018). ...
Article
Full-text available
In most small-scale fisheries (SSF), there is limited or null information about the distribution and spatial extent of the fishing grounds where the fleets operate, due to the lack of explicit spatial and temporal data. This information is key when addressing marine spatial planning and fisheries management programs for SSF. In addition to technical or biogeographic restrictions, environmental conditions in the area influence the way fishers operate. Making use of data from a pilot Vessel Monitoring System (VMS) project tested in a small-scale fleet in the Southeastern Gulf of Mexico (SGoM), for the first time in the region, we were able to learn what role environmental factors play in the distribution of potential fishing grounds for this fleet. We got tracking information of 1,608 daily fishing trips from vessels operating in four states using the VMS for 7 months. We used a correlative modeling approach to identify potential fishing grounds where this fleet operates along the SGoM, accounting for environmental variables. We assumed that environmental conditions can shape the spatial distribution of species targeted by this fleet and hence influence fishers’ operations. The results indicated that net primary production and sea surface temperature were the main drivers that shape the spatio-temporal potential distribution of fishing grounds in the study site. The approach employed here seems appropriated and opens an opportunity to learn more about the factors that define the spatial distribution of small-scale fleets and their potential fishing grounds.
... At each of the six coastal sites, a baseline of fishing activities will be collected from participating fishers including trip frequency, duration, method, location, and substrate type, along with the catch volume. Data will be collected from fishers as they return to shore by on-site enumerators, consistent with the national fisheries monitoring system in Timor-Leste, PeskAAS (following [26]). This is a tablet-based survey where government enumerators collect and upload near-real-time landings data to the central database. ...
... 3. Average catch rates calculated as the increase in fish availability by comparing average catch rates prior to FAD deployment with catch rates post-deployment. Catch rate and total production values for different coastal community sites will be calculated and extrapolated following Tilley et al. [26]. ...
Article
Full-text available
Timor-Leste is one of the world’s most malnourished nations where micronutrient-deficient diets are a contributing factor to the prevalence of child stunting, currently estimated to be 45.6% of children under five. Fish are an important source of nutrients and one that may assist the country’s predominantly rural population of agriculturalists to exit poverty and malnutrition. However, a small national fishing fleet producing low catch volumes places fish out of reach of most inland and upland populations where it is needed most. Fish consumption is very low in rural, inland areas compared to coastal, regional, and global averages. This study is a one-year, partially masked, cluster-randomized controlled trial among families living in rural, inland Timor-Leste. We aim to test and compare the effects of two treatments, alone and in combination, on the frequency and volume of household fish consumption in rural, inland areas as a proxy for improved dietary diversity and micronutrient intake. Treatment 1 is the installation of nearshore, moored fish aggregating devices (FADs) to improve catch rates with existing fishing gears. Treatment 2 is a social and behaviour change (SBC) activity to promote fish consumption. Villages in inland communities will be randomized to receive treatment 1, treatment 2, both treatments, or neither treatment. Data will be collected at baseline (prior to the rollout of the treatments) and endline. Our study will determine the impact of an improved supply of fish, along with nutrition-oriented SBC activities, on the fish purchasing and consumption practices of rural, inland households. Findings from this study are urgently needed by Small Island Developing States to guide policy and investment decisions on how best to improve households’ diets using locally available, nutrient-dense foods such as fish. Investments such as these are needed to break the cycle of malnutrition. This trial is registered at clinicaltrials.gov (NCT04729829). Trial registration: Trial registered at clinicaltrials.gov Identifier: NCT04729829 .
... Recent digital interventions have: enabled new income streams for women in SSF communities in Bangladesh through digital financial inclusion (FAO and WorldFish, 2020, chap. 7); brought about improved social cohesion through community-supported fisheries in South Africa (Nthane et al., 2020;Stone, 2020); have automated faster, cheaper and more accurate scientific guidance for fisheries management (Tilley et al., 2020); and provided high-resolution coastal zoning for co-management . However, these systems and interventions need not cost the earth. ...
... However, these systems and interventions need not cost the earth. Tilley et al. (2020) describe PeskAAS, a decision support tool built in the open-source R coding environment that provides nearreal-time, automated catch analytics, and that has been adopted as the national fisheries monitoring system of Timor-Leste. The PeskAAS system makes use of a free platform to collect data through webforms 5 called Kobo Toolbox built on the Open Data Kit (ODK) framework. ...
Article
Full-text available
Adaptive, inclusive and effective management of fisheries resources is dependent on knowledge from multiple quantitative and qualitative sources. As technology advances, there is an increasing interest in digital and automated solutions for gathering fisheries data. Small-scale fisheries (SSF) have presented a persistent challenge to many centralized quantitative data collection systems, and frequently maintain the status of 'unreported'. This unreported nature often implicates SSF in the definition and discussions of illegal, unreported and unregulated (IUU) fishing. Monitoring, control and surveillance are seen as a vital part of the solution to IUU fishing, with substantial investment being put into increasingly sophisticated technology for tracking fishing vessels. For the past few years, India has been attempting to pass legislation to require all vessels, from small-scale to industrial, to install vessel monitoring systems on the grounds of national security and combating IUU fishing. However, there are concerns that a securitized and top-down approach to implement vessel tracking is not only wasteful but risks further marginalization of small-scale fishers from the resource, and fisheries groups from governance processes. India should seek to solve the underlying causes of IUU fishing while also developing collaborative monitoring and community-based management models. In this paper, we review evidence of emerging information and communication technologies and approaches in SSF and discuss how, if introduced and managed through collaborative processes, they could be used as a platform to strengthen inclusive governance, increase sustainability and improve wellbeing in coastal fisheries in India. (
... At each of the six coastal sites, a baseline of fishing activities will be collected from participating 180 fishers including trip frequency, duration, method, location and substrate type, along with the 181 catch volume. Data will be collected from fishers as they return to shore by on-site enumerators, 182 consistent with the national fisheries monitoring system in Timor-Leste, PeskAAS (following [24]). 183 ...
... total production values for different coastal community sites will be calculated and 243 extrapolated following Tilley et al. [24]. assessing knowledge of nutritional benefits of fish and handling practices will be 249 administered to respondents at baseline, midline and endline. ...
Preprint
Full-text available
Timor-Leste is one of the world's most malnourished nations where micronutrient-deficient diets are a contributing factor to the prevalence of child stunting, currently estimated to be 45.6% of children under five. Fish are an important source of micronutrients and one that may assist the country's predominantly rural population of agriculturalists to exit poverty and malnutrition. However, a small national fishing fleet producing low catch volumes place fish out of reach of most inland and upland populations where it is needed most. Fish consumption is very low in rural areas compared to coastal, regional, and global averages. This trial is a one-year, cluster-randomized, partially masked, controlled trial among families living in rural, inland Timor-Leste. This trial aims to test and compare the effects of two treatments, alone and in combination, on the frequency and volume of household fish consumption in upland areas as a proxy for improved dietary diversity and micronutrient intake. Treatment 1 is the installation of nearshore, moored fish aggregating devices (FADs) to improve catch rates with existing fishing gears. Treatment 2 is social and behaviour change (SBC) activities to promote fish consumption. Villages in inland communities will be randomized to receive treatment 1, treatment 2, both treatments, or neither treatment. Some households with one child under five will be recruited, and data will be collected at baseline (prior to the rollout of the treatments) and endline. Our study will determine the impact of an improved supply of fish, along with nutrition-oriented SBC activities, on the fish purchasing and consumption practices of rural, inland households. Findings from this study are urgently needed by small island developing states in order to make policy and investment decisions on how best to improve households' diets using locally available, nutrient-dense foods such as fish. Investments such as these are needed to break the cycle of malnutrition.
... Furthermore, Timorese common names listed on FishBase also include similar name 'kopi' for fusiliers of the Caesionidae family, as well as for Tropical Pilchard A. sirm (Froese and Pauly, 2020). National fisheries monitoring in Timor-Leste currently records landings at the family level due to the difficulties associated with accurately identifying mixed-species catches to species level across the country (Tilley et al., 2020). However, this example illustrates that the same or similar local name could relate to three different families. ...
... However, this example illustrates that the same or similar local name could relate to three different families. Findings from this study suggest landings of 'sardiña kobi' are most likely to be members of the Clupeidae family, either Sardinella spp. in Bobonaro municipality or Amblygaster spp. in Dili-but not 'herring' from the 'Elopidae family, ' as currently recorded (Tilley et al., 2020;WorldFish and MAF, 2021). We cannot provide any insight on where this name may relate to fusiliers or oxeye herring. ...
Article
Full-text available
Tropical sardines (Family Clupeidae) are an important component of many marine fisheries in the Indo-West Pacific region. In Timor-Leste, a small, less-developed country within this region, ‘sardiña’ are some of the more commonly caught and consumed fish. Yet there is little published information from Timor-Leste about the species composition of these fisheries, nor their biology or ecology. We document the knowledge of Timorese fishers on nine locally distinguished sardine types that contribute to fisheries, and relate these to at least nine species: four species of ‘Flat-bodied Sardinellas’ (Sardinella subg. Clupeonia spp.), one species of ‘Round-bodied Sardinella’ (Sardinella subg. Sardinella lemuru), two species of ‘Tropical Pilchards’ (Amblygaster spp.) and a ‘Tropical Herring’ species (Herklotsichthys quadrimaculatus), all from the Clupeidae family; and one Dussumieria species from the Dussumieriidae family. We record variations in local sardine names across the country and document aspects of fishers’ knowledge relevant to understanding and managing the fisheries, including local sardine species’ seasonality, habitat, movements, interannual variation, as well as post-harvest characteristics in relation to perishability. In general, local names relate more closely with groups of species than individual species, although some names also distinguish fish size within species-groups. The local knowledge identified in this study has immediate application to inform fisheries monitoring and management, and to identify areas for future research. Notably, Timorese fishers recognize and make use of the strong association between some sardine species-groups and seasonally turbid river plumes. While further research is required to understand the underlying mechanisms of this association, this emphasizes the need to consider coastal fisheries and fisher livelihood impacts when assessing any plans or proposals that may alter river flow or water quality. Fishers also recognize migratory behavior of some sardine species, in particular the Flat bodied Sardinellas (S. gibbosa and others) along the north-west coast of Timor-Leste and across the border into Indonesian West Timor. Such insights complicate and need to be accounted for in initiatives for co-management or community-based management of Timor-Leste’s coastal waters and their fisheries.
... It is widely recognised that self-reporting offers an opportunity to cover a larger proportion of the fleet with lower costs compared to observer programmes (Starr 2010). Moreover, high quality data, comparable to those collected by observer sampling has been achieved in several fisheries (Starr and Vignaux 1997;Hoare et al. 2011;Mion et al. 2015;Campbell et al. 2021;Marshall et al. 2021;Tilley et al. 2020). However, self-reporting has also been criticised; specifically, the lack of time, motivation and training of fishers which may lead to inaccurate reporting (Lordan et al. 2011;Sampson 2011;Mangi et al. 2015). ...
Article
Full-text available
About a third of all marine fish in the world are caught in Small-Scale Fisheries (SSF). SSF are increasingly recognised as essential for food security and livelihoods for vulnerable and economically fragile communities globally. Although individual SSF vessels are usually perceived as having little impact on the ecosystem, the cumulative impact of gear type and number of vessels may be substantial. Bottom trawling is a common fishing method that can greatly influence the marine ecosystem by damaging the seafloor and generating high levels of discards. However, appropriate sampling coverage using on-board observer programmes to collect these data from SSF are rare, as they are expensive and pose logistical constraints. A mobile App was used to assess whether self-reporting by fishers could provide reliable fine-scale information on fishing effort and discards over time in an illegal shrimp trawling fishery in northern Peru. Maps depicting the spatial distribution of trawling effort and the proportion of discards from observers and fishers were compared using the Similarity in Means (SIM) Index, which ranges from 0 when spatial patterns differ completely to 1 when spatial patterns are very similar. High levels of agreement between spatio-temporal patterns of effort (SIM Index = 0.81) and discards (0.96) were found between fisher and observer maps. Moreover, far greater spatial coverage was accomplished by fishers, suggesting that self-reporting via an App represents a useful approach to collect reliable fisheries data as an initial step for effective monitoring and management of these fisheries. Supplementary information: The online version contains supplementary material available at 10.1007/s11160-022-09708-9.
... Although in recent times monitoring systems based on mobile application represent a tempting and promoted solution [68], especially in those countries where the fishery regulatory system is not more effective [69][70][71], we believe that, where possible, a device directly connected to a vessel engine should allow a more-reliable data acquisition. Indeed, looking toward the application of a legislation that provides for the mandatory monitoring of small-scale fishing vessels, it would be unthinkable to rely on official data collection on personal devices, which can be voluntarily turned off or involuntarily damaged. ...
Article
Full-text available
During the last decade vessel-position-recording devices, such as the Vessel Monitoring System and the Automatic Identification System, have increasingly given accurate spatial and quantitative information of industrial fisheries. On the other hand, small-scale fisheries (vessels below 12 m) remain untracked and largely unregulated even though they play an important socio-economic and cultural role in European waters and coastal communities and account for most of the total EU fishing fleet. The typically low-technological capacity of these small-scale fishing boats—for which space and power onboard are often limited—as well their reduced operative range encourage the development of efficient, low-cost, and low-burden tracking solutions. In this context, we designed a cost-effective and scalable prototypic architecture to gather and process positional data from small-scale vessels, making use of a LoRaWAN/cellular network. Data collected by our first installation are presented, as well as its preliminary processing. The emergence of a such low-cost and open-source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data, and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management.
... As cellular coverage and cell phone usage spreads globally, smartphone apps are being implemented that increase coral reef fisheries data collection, including gathering and reporting catch data (Frost, 2017;Jeffers et al., 2019), monitoring illegal, unreported, andunregulated fishing (Environmental Justice Foundation, 2014;Nisanala et al., 2015;CEA, 2020b), and mapping fishing grounds (Paul et al., 2016). Several smart phone apps have been developed to date, including (1) Abalobi that provides (non-reef) small-scale fisheries in South Africa with a platform for data collection, supply chain traceability, real-time market information, and direct access to purchasers (UNESCO, 2018), (2) OurFish created by Rare that acts as a fisheries monitoring system and finance management system for reef fisheries in Honduras and Belize 1 , (3) PeskAAS created by WorldFish that acts as a monitoring system for small-scale fisheries in Timor-Leste and could be used as a platform for providing fishers with market information (Tilley et al., 2020), and (4) PescaData created by COBI which allows small-scale fishers in Mexico to monitor effort and catch, obtain real-time market information, and have direct access to the marketplace 2 . The implementation of smartphone apps for reef fishers in additional geographies could support fishery management and monitoring, empower fishers, and increase fisher livelihoods, ultimately leading to more sustainable practices. ...
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