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Using GIS and stakeholder involvement to innovate marine mammal bycatch risk assessment in data-limited fisheries

Authors:
  • The Scripps Institution of Oceanography
  • The MareCet Research Organization

Abstract and Figures

Fisheries bycatch has been identified as the greatest threat to marine mammals worldwide. Characterizing the impacts of bycatch on marine mammals is challenging because it is difficult to both observe and quantify, particularly in small-scale fisheries where data on fishing effort and marine mammal abundance and distribution are often limited. The lack of risk frameworks that can integrate and visualize existing data have hindered the ability to describe and quantify bycatch risk. Here, we describe the design of a new geographic information systems tool built specifically for the analysis of bycatch in small-scale fisheries, called Bycatch Risk Assessment (ByRA). Using marine mammals in Malaysia and Vietnam as a test case, we applied ByRA to assess the risks posed to Irrawaddy dolphins (Orcaella brevirostris) and dugongs (Dugong dugon) by five small-scale fishing gear types (hook and line, nets, longlines, pots and traps, and trawls). ByRA leverages existing data on animal distributions, fisheries effort, and estimates of interaction rates by combining expert knowledge and spatial analyses of existing data to visualize and characterize bycatch risk. By identifying areas of bycatch concern while accounting for uncertainty using graphics, maps and summary tables, we demonstrate the importance of integrating available geospatial data in an accessible format that taps into local knowledge and can be corroborated by and communicated to stakeholders of data-limited fisheries. Our methodological approach aims to meet a critical need of fisheries managers: to identify emergent interaction patterns between fishing gears and marine mammals and support the development of management actions that can lead to sustainable fisheries and mitigate bycatch risk for species of conservation concern.
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RESEARCH ARTICLE
Using GIS and stakeholder involvement to
innovate marine mammal bycatch risk
assessment in data-limited fisheries
Gregory M. VerutesID
1,2
*, Andrew F. Johnson
3,4
, Marjolaine CaillatID
5
, Louisa
S. Ponnampalam
6
, Cindy Peter
7
, Long VuID
8
, Chalatip Junchompoo
9
, Rebecca
L. Lewison
10
, Ellen M. Hines
11
1Faculty of Political and Social Sciences, Universidade de Santiago de Compostela, Santiago de
Compostela, Spain, 2Campus Do*Mar, International Campus of Excellence, Vigo, Spain, 3MarFishEco
Fisheries Consultants, Edinburgh, United Kingdom, 4The Lyell Centre, Institute of Life and Earth Sciences,
School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, United
Kingdom, 5Environmental Defense Fund, San Francisco, CA, United States of America, 6The MareCet
Research Organization, Shah Alam, Malaysia, 7Institute of Biodiversity and Environmental Conservation,
University Malaysia Sarawak, Sarawak, Malaysia, 8Vietnam Marine Megafauna Network, Center for
Biodiversity Conservation and Endangered Species, Ho Chi Minh, Vietnam, 9Department of Marine and
Coastal Resources, Rayong, Thailand, 10 Department of Biology, San Diego State University, San Diego,
CA, United States of America, 11 Estuary & Ocean Science Center, San Francisco State University, Tiburon,
CA, United States of America
*gregg@marfisheco.com
Abstract
Fisheries bycatch has been identified as the greatest threat to marine mammals worldwide.
Characterizing the impacts of bycatch on marine mammals is challenging because it is diffi-
cult to both observe and quantify, particularly in small-scale fisheries where data on fishing
effort and marine mammal abundance and distribution are often limited. The lack of risk
frameworks that can integrate and visualize existing data have hindered the ability to
describe and quantify bycatch risk. Here, we describe the design of a new geographic infor-
mation systems tool built specifically for the analysis of bycatch in small-scale fisheries, called
Bycatch Risk Assessment (ByRA). Using marine mammals in Malaysia and Vietnam as a
test case, we applied ByRA to assess the risks posed to Irrawaddy dolphins (Orcaella brevir-
ostris) and dugongs (Dugong dugon) by five small-scale fishing gear types (hook and line,
nets, longlines, pots and traps, and trawls). ByRA leverages existing data on animal distribu-
tions, fisheries effort, and estimates of interaction rates by combining expert knowledge and
spatial analyses of existing data to visualize and characterize bycatch risk. By identifying
areas of bycatch concern while accounting for uncertainty using graphics, maps and sum-
mary tables, we demonstrate the importance of integrating available geospatial data in an
accessible format that taps into local knowledge and can be corroborated by and communi-
cated to stakeholders of data-limited fisheries. Our methodological approach aims to meet a
critical need of fisheries managers: to identify emergent interaction patterns between fishing
gears and marine mammals and support the development of management actions that can
lead to sustainable fisheries and mitigate bycatch risk for species of conservation concern.
PLOS ONE
PLOS ONE | https://doi.org/10.1371/journal.pone.0237835 August 20, 2020 1 / 25
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OPEN ACCESS
Citation: Verutes GM, Johnson AF, Caillat M,
Ponnampalam LS, Peter C, Vu L, et al. (2020)
Using GIS and stakeholder involvement to innovate
marine mammal bycatch risk assessment in data-
limited fisheries. PLoS ONE 15(8): e0237835.
https://doi.org/10.1371/journal.pone.0237835
Editor: Daniel E. Duplisea, Maurice Lamontagne
Institute, CANADA
Received: December 5, 2019
Accepted: August 4, 2020
Published: August 20, 2020
Copyright: ©2020 Verutes 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: Data cannot be
shared publicly because of legal and ethical
reasons. Researchers who work in Sarawak and
Johor, Malaysia, especially those working with
medicinal plants and rare animals do not make
GPS points publicly available because individuals
may use it to illegally collect the plant or animal and
exploit it commercially which is detrimental to
species survival and conservation. GPS locations
of dugongs and dolphins make them easily
accessible to collectors and hunters. Additionally,
under Sarawak’s Wildlife Protection Ordinance
1. Introduction
Small-scale fisheries (SSF) are a critical means of subsistence and livelihood in many regions of
the world. They provide needed sources of protein, food security, and poverty alleviation [1,
2], and support the well-being of more than half a billion people worldwide [3]. Despite their
importance globally, SSF struggle with sustainability when local communities do not have
access to the social capital necessary to participate in resource management [46]. As a result,
information and data about SSF is often limited as compared to large-scale, industrial fishing
operations [79]. Furthermore, some SSF have been identified as a threat to marine ecosystems
and species [1012]. Given the tenuous status of many coastal-marine species and the socio-
economic importance of SSF, robust frameworks are needed to support sustainable fisheries
and species conservation in SSFss [1315].
SSF, like other fisheries sectors, incurs fisheries bycatch. Bycatch refers to the unintended
capture of non-target species [16,17], and it has been identified as the largest threat to marine
mammals globally [18,19]. For depleted marine mammal populations, even a few entangle-
ments per year can pose a significant threat [20], especially when combined with cumulative
impacts from other anthropogenic threats [2123]. Bycatch risk is particularly challenging to
analyze and calculate in SSF because of data gaps on fishing effort and marine mammal distri-
bution and ranges [9,19,24]. Species conservation research developed in close collaboration
with local stakeholders, agency personnel, and scientists can be used to overcome these obsta-
cles by characterizing the relationship between SSF and the distribution of threatened marine
mammals [2527].
Recent innovations in geospatial technology have demonstrated success in supporting sus-
tainable fisheries and marine mammal conservation. Global positioning systems coupled with
unmanned aerial and marine drones equipped with laser, thermal, and acoustic sensors now
enable scientists and conservation practitioners to track marine megafauna movements [28
30] and SSF fishing effort [31], map species distribution and habitat preferences [32,33] and
estimate taxa-specific impacts from human activities [3436]. In addition, community involve-
ment and local expertise can be integrated with remote sensing and spatial analyses to fill data
gaps, characterize uncertainty of existing information, and produce actionable information to
address sustainability challenges [37,38]. Given the growing availability of tools to manage
SSF, including those that draw on local knowledge and geographic information systems (GIS),
we aim to integrate and visualize existing data on SSF interactions with marine mammals to
increase the efficacy of fisheries management research and reduce uncertainty.
A risk assessment evaluates the likelihood, or probability, of an event happening and the
magnitude of the consequences if the event happens [39,40]. Species risk assessment is one
approach to support sustainable resource use and conservation by evaluating the risk-reduc-
tion potential of different fisheries management options in marine fish stocks, habitats, and
ecosystems [4143]. With a similar goal in mind, geographers and spatial ecologists have
developed tools to map and measure the probability of exposure, and resulting vulnerabilities
to marine species, from offshore wind farm impacts and vessel noise to fisheries bycatch [44
46]. Studies that use GIS to evaluate risk of these incidental interactions can help address the
marine mammal bycatch problem because they present frameworks for the analysis of biodi-
versity and its susceptibility to one or more threats. Further, spatially explicit marine species
risk assessments (e.g., [43,47,48]) draw on participatory mapping, spatial analysis and data
visualization techniques, which are particularly important in data-limited contexts [38,49], to
engage stakeholders, establish trust, and access local knowledge [50,51].
Despite advances in GIS technology for data collection, spatial analysis, and risk assessment,
there remains a need for tools that incorporate the spatio-temporal dimension of SSF and
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1998, cetaceans are a Totally Protected Species. As
such, the authors feel strongly that their locality
should be placed under protection as well. In line
with the goal of ensuring long-term data availability
to all interested researchers, the following three
institutional representatives, who did not
collaborate in the study and are not listed as
authors on the manuscript, are willing to hold the
data and respond to external requests for data
access. 1) Fairul Izmal Jamal Hisne (ask.
marecet@gmail.com) - SBTI, Malaysia 2) Gianna
Minton (gianna.minton@gmail.com) - KUCG,
Malaysia 3) Truong Anh Tho
(truonganhtho@gmail.com) - KGBR, Vietnam To
ensure persistent or long-term data storage and
availability, data will be stored in three independent
locations. If a contact is not responsive by e-mail,
the interested parties are encouraged to contact the
lead author (gverutes@gmail.com, +1 202-709-
3457) to assist with access to these sensitive data
on the locations of endangered species.
Funding: Data collection in the Sibu-Tinggi field
site was funded by the Pew Charitable Trusts via a
Pew Marine Fellowship to LSP and the Dugong &
Seagrass Conservation Project of the Global
Environment Fund (GEF). Data collection in the
Kuching Bay field site was funded by Sarawak Shell
Berhad, Ocean Park Conservation Foundation,
International Whaling Commission and the Ministry
of Science, Technology and Innovation, Malaysia.
Funding for this project was from the U.S. National
Office of Atmospheric Administration Office of
International Affairs and Seafood Inspection Award
Number: NA16NMF4630338.
Competing interests: The authors have declared
that no competing interests exist.
include both bycatch exposure and its consequences to resident marine mammal populations.
For this reason, we developed the Bycatch Risk Assessment (ByRA), a tool for spatially explicit
risk assessment tailored specifically for marine mammal bycatch in data-limited fisheries.
ByRA combines existing SSF and marine mammal data within an open source GIS-based
framework. Most importantly, outputs from the ByRA, which include bycatch risk maps and
plots describing species-gear interactions during different seasons and scenarios, are produced
in accessible interactive web visualization and summary table formats. These products can
therefore easily be communicated to non-expert stakeholders and vetted by experts for itera-
tive improvement that augments the understanding of local fisheries, identifies and fills knowl-
edge gaps, and can be used for designing strategies to meet sustainability objectives [25,52,
53].
Here, we present the results from an application of ByRA in Southeast Asia, specifically
Malaysia and Vietnam. In these countries, fish are a major source of nutrition and livelihoods
[54,55] and managers strive to assemble reliable, accurate, and spatially explicit information
about SSF bycatch [19,25]. The ByRA has two important characteristics that make it useful for
understanding the distribution of fishing activities, marine mammals, and their interaction
rates over space and time: (1) it is designed for rapid spatial assessment at the site scale, with
SSF data inputs and assumptions communicated in a transparent manner; and (2) it is devel-
oped in close collaboration with local stakeholders, agency personnel, and scientists, which is
critical for actionable evaluation of different bycatch management interventions and strategies.
ByRA facilitates stakeholder engagement, identifies areas of bycatch concern, and co-creates
knowledge. We describe how ByRA can be used to leverage existing data on animal distribu-
tions and fisheries effort, integrate participatory mapping and local expert knowledge within
an open source GIS framework.
2. Materials and methods
To develop the ByRA tool, we used three case study sites in Southeast Asia and the bycatch of
two species of marine mammals. The sites were Kien Giang Biosphere Reserve (Vietnam),
Kuching Bay and the Mersing Archipelago (Malaysia) (Fig 1). The first two experience bycatch
of Irrawaddy dolphins (Orcaella brevirostris), and the latter has a significant dugong (Dugong
dugon) population. At each of the field sites, we had the support of and worked collaboratively
with local researchers and resource management agencies to undertake this work and use their
data on marine mammal and small-scale fisheries vessel occurrence.
Bycatch risk was assessed from existing GIS data on marine mammal occurrence and fish-
ing activities. These were mapped as habitat suitability and kernel density estimations, respec-
tively. Fishing data were organized into five general gear categories (hook and line, nets,
longlines, pots and traps, and trawls). Drivers of bycatch risk included environmental and
sociopolitical factors, specifically seasonal weather (monsoonal) patterns and the current status
of fisheries management that influence the distribution of marine mammal populations and
SSFs. In Kuching Bay, Malaysia we assessed three scenarios (post monsoon, dry, and pre mon-
soon) to capture seasonal drivers known to change fishing patterns and Irrawaddy dolphin
occurrence over time and, therefore, vary their exposure to bycatch risk, including encounter
rates and timing of overlap with different gears in space. In all sites we identified consequences
(impacts) of such interactions, including life history stages affected by gear-specific threats and
local conservation status of the species. The following sections describe three key steps we took
to leverage existing data with the ByRA tool: (i) engage stakeholders and acquire existing
knowledge, (ii) build bycatch scenarios, and (iii) analyze and visualize bycatch risk and data
uncertainty.
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2.1. Engage stakeholders and acquire existing knowledge
Our methodological approach emphasized transparency and building collaborative relation-
ships prior to acquiring data, including representatives from provincial governments, non-
governmental organizations (NGOs), and scientists from local universities (Table 1). When
Fig 1. Three field sites selected in Southeast Asia. Areas of bycatch concern shown in black.
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Table 1. Summary of each field site, including focal species, situational context, and partner(s).
Abbreviated and full name Focal species Situational context Partner(s) and publications
A) SBTI: Sibu-Tinggi
Islands, Mersing
Archipelago, Johor,
Malaysia
Dugongs (Dugong
dugon)
The Mersing Archipelago lies along the southeast coast
of Peninsular Malaysia; partners have been studying
dugongs and the social science of dugong conservation
since 2014
The MareCet Research Organization, Malaysia
(Ponnampalam et al. [56])
B) KUCG: Kuching Bay,
Sarawak, Malaysia
Irrawaddy dolphins
(Orcaella
brevirostris)
Expansive estuarine system near the city of Kuching;
partners have been studying cetaceans in Kuching Bay
since 2008
Sarawak Dolphin Project; Institute of Biodiversity and
Environmental Conservation, Universiti Malaysia
Sarawak (Minton et al. [20]; Peter et al. [57])
C) KGBR: Kien Giang
Biosphere Reserve, Vietnam
Irrawaddy dolphins
(Orcaella
brevirostris)
A Biosphere Reserve designated by UNESCO in 2007;
the area experiences one of the highest levels of fishing
in the country; the Vietnam Marine Megafauna
Network regularly monitors the waters of KGBR
Kien Giang Biosphere Reserve, Vietnam; Southern
Institute of Ecology (Vietnam Academy of Science and
Technology); Vietnam Marine Megafauna Network
(Center for Biodiversity Conservation and Endangered
Species) (Vu et al. [58])
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requesting information, we described our intended use and respected constraints on sharing
data based on local management hierarchy, and how it would be reported. For example, we
received sensitive information (e.g., animal occurrence) in part because data holders were
involved in the project from its inception, understood how the knowledge would be used, and
knew their participation would be anonymized through the tool.
The following questions posed during the data acquistion phase yielded the most useful
information to perform the ByRA: (i) What kinds of surveys and technology do you use to
track marine mammals and fishing vessels? (ii) Which fisheries are present and what fishing
gears are used? (iii) Which spatial data are available/exist for your field site to help understand
risk of bycatch (e.g., sightings of marine mammals, bathymetric soundings from nautical
charts, fisheries management guidelines)? (iv) Does the area have an existing fisheries observer
program, stranding network or other indications of work related to marine mammals and, if
so, in which season(s) is monitoring conducted?
2.1.1. Areas of interest and subregions. Based on the spatial coverage of existing marine
mammal and fisheries surveys, we structured the risk assessment by delineating areas of inter-
est (AOI) that extended 10 km beyond locations with high SSF and marine mammal occur-
rence. This minimized edge effects in the geospatial calculations and focused on known areas
of bycatch concern. To summarize and compare ByRA findings within each site, we defined
between 4 and 8 subregions (Fig 2) that differed by ecological, environmental, and/or gover-
nance factors. The AOI for Sibu-Tinggi Islands (SBTI) spanned the existing Sultan Iskandar
Marine Park and surrounding waters covering the extent over which the MareCet Research
Organization conducts aerial distributional line transect surveys of dugongs and fishing activi-
ties [56]. We divided the Kuching Bay site (KUCG) into four subregions as in Peter et al. [57]
to capture two ecologically distinct coastal and two hydrologically connected inland areas of
Kuching’s expansive riverine system. The AOI for Kien Giang Biosphere Reserve (KGBR) is
the entire biosphere reserve, which was subdivided following the survey strata used by the
Vietnam Marine Megafauna Network [58].
2.1.2.Fishing activities and gear usage. We acquired information about fishing gears
known to entangle, cause strandings or mortality of the two coastal marine mammals of inter-
est. All fishing methods were organized into five broad but distinct categories: (1) nets, (2)
trawls, (3) pots and traps, (4) longlines, and (5) hook and line (Table 2). Combining diverse fish-
ing methods into five general gear categories streamlined the data acquisition process by help-
ing local partners identify the most common fishing techniques in each site. For instance, we
initially identified more than 20 different gears used by fishers inside KGBR, Vietnam and
neighboring Cambodian waters [58]. These five groups of fishing activities known to encounter
marine mammals served to elucidate gear-specific impacts during the expert judgement stage.
2.1.3. Environmental and marine mammal sightings data. We began by compiling glob-
ally-available GIS layers (e.g., continental land mass and islands, major rivers, bathymetry) to
characterize the coastal-marine environment in the study areas. Three online sources, in par-
ticular, (1) Natural Earth (naturalearthdata.com), (2) GADM (gadm.org) and (3) GPS Nautical
Charts (fishing-app.gpsnauticalcharts.com) offered free reference layers and viewers to iden-
tify available nautical charts for purchase. Monitoring efforts by local partners documented
sightings of animals and SSF during aerial and boat-based surveys between 2008 and 2016.
These data included GPS location (latitude/longitude), individuals observed (number) or gear
type (name), and time of year (season) for each recorded sighting (Fig 3; S1 Table in S1 Data).
Drawings of fishing grounds by fishers and government officers were also acquired for the
SBTI and KGBR sites.
To prepare these layers for input to ByRA, we leveraged several spatial data processing rou-
tines in QGIS, an open source GIS software platform [59]. This included georeferencing and
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digitizing depth soundings from nautical charts, cost distance analysis, and inverse distance
weighting for calculating distance to land and river mouths and producing bathymetric inter-
polation surfaces. Sightings of marine mammals coupled with environmental data were used
in habitat suitability models to estimate their distribution and relative abundance in two
Malaysian field sites (see Section 2.2.1. ‘Habitat models’).
2.2. Build bycatch scenarios
Scenarios are simplified descriptions of the present and possible futures [60]. In this applica-
tion of ByRA, we used scenario layers assembled in GIS to highlight suitable habitat areas for
marine mammals and the current distribution and intensity of fishing activities by gear type.
The scenarios included environmental and socio-political factors such as seasonal monsoons
and fishing regulations, e.g., gear restrictions and sensitive habitat zones that can influence the
behaviors of fishers and marine mammals. The resulting scenario layers captured emergent
patterns of species-gear interactions and were subsequently evaluated in three separate bycatch
risk assessments (see Section 2.3: ‘Assess and visualize bycatch risk and data uncertainty’).
2.2.1. Habitat suitability. Habitat models are important tools to link marine mammal
observations to environmental variables and identifying critical habitat [61,62]. To estimate
Fig 2. Subregions (numbered circles) based on management, conservation, geopolitical and ecological similarities across the
three SE Asian field sites. A) SBTI: Zones 1–3 delineate the 2 nautical mile boundaries of the existing Sultan Iskandar Marine Park.
Zone 4 covers the remaining core dugong ranging areas as monitored by The MareCet Research Organization in Johor, Malaysia; (B)
KUCG: 1) Santubong-Salak Bay, 2) Bako-Buntal Bay, 3) Salak Telaga Air Rivers, 4) Santubong-Buntal Rivers as in Peter et al. [57].
Darkest grey circles indicate the river network and estuaries of Kuching Bay; (C) KGBR: Zones 1–7 based on survey strata of the
Vietnam Marine Megafauna Network.
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the distribution and relative abundance of dugongs and Irrawaddy dolphins in geographical
space, we used species distribution models suitable for small sample sizes [48,63,64], or a
rule-based GIS approach for habitat suitability designed for data-limited situations. Depth, dis-
tance to land, and distance to river mouths have been shown by numerous researchers to be
commonly important measures of habitat suitability for dugongs and coastal cetaceans, includ-
ing Irrawaddy dolphins (see [20,48,65]). The selection of the appropriate habitat model, to
identify the most important areas within the distribution of a species, is site and dataset-spe-
cific [66] and good predictive ability has been achieved with parsimonious models [67].
When marine mammal sightings were available, we applied the Maxent modeling software
(biodiversityinformatics.amnh.org/open_source/maxent/) to map suitable environmental con-
ditions. Maxent needs 30 or more sightings for reasonable statistical power [32,63] and this
quantity of occurrence data existed for both Malaysian field sites. Presence-only data of Irra-
waddy dolphins and dugongs occurrence were used to quantify the statistical relationship
between predictor environmental covariates at locations where a species had been observed
versus background locations in which no observation was done [68]. The Maxent algorithm
inferred species distribution as a function of relevant environmental covariates [69], which in
the SBTI and KUCG sites were water depth (m), seafloor slope (degrees), and/or distance to
land and river mouths (km). Next, we converted Maxent outputs from continuous to categori-
cal data in order to match rating scores for each species-gear interaction (see Section: 2.3.2.
‘Spatially explicit criteria’). Habitat suitability layers were reclassified 1 to 3 (lowest to highest
suitability) based on the omission rate threshold of 10% (10% of the training occurrence data
classified in non-suitable habitats) and the maximum relative occurrence rate (maximum
probability for a species to be in a suitable habitat). Maxent variable selection, model testing,
Table 2. Fishing methods and corresponding gear categories identified in each SEA field site.
Gear category A) SBTI B) KUCG C) KGBR
Nets drift net gillnet (“pukat / ranto”) anchovy purse seine
mackerel purse seine
set net—nylon purse seine with light
bottom gillnet
surface gillnet
purse seine drift net (“tangsi”) shrimp gillnet
small size trammel net
sardine gillnet
trammel net (“pukat 3 lapis / jaring”) crab gillnet
crab trammel net
mosquito net
set netgillnet
Trawls trawl net trawl net single (“normal”) trawl
pair trawl
electric trawl
Pots and traps trap pots and traps crab trap
cuttlefish trap
octopus trap
rat tail
Longlines bottom line longline—high: (“rawai timbul”) fish hooks and lines
longline—low (“rawai tenggelam”)
Hook and line line fishing rod line (“pancing”) squid hooks and lines
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performance evaluation and validation is described in the Supporting Information (1.2 ‘Spatial
data on species for the spatial overlap criterion’; S2 and S4 Tables in S1 Data; S7, S8, S9 and
S10 Figs in S1 Data).
There were insufficient observations of Irrawaddy dolphins (n = 2) for a correlative model
in Vietnam. Instead, we employed a ‘low-data’ approach to map suitable habitat areas for Irra-
waddy dolphins in KGBR based on Briscoe et al. [48], which used a rule-based GIS analysis to
designate areas of marine mammal habitat in an area with limited sightings data. The Union
tool in QGIS was used to map the overlap between bathymetry and cost distance layers for
depicting levels of habitat use in KGBR, specifically: (i) depth range (0–15m), (ii) proximity to
major river mouths (<25km) and (iii) proximity to land (<10km) based on previous Irra-
waddy dolphin research [20,65,70] (S3 Table, S4 Fig in S1 Data).
2.2.2. Seasonality. To account for changes in fishing activity and Irrawaddy dolphin habi-
tat use throughout the year, we defined seasonal scenarios in KUCG and analyzed species-gear
interactions over three distinct periods of time–i.e., post-monsoon (March to May), dry season
(May to September), and pre-monsoon (September to December). In SBTI, aerial surveys to
monitor dugongs and SSF around the Sibu-Tinggi Islands were conducted during the dry sea-
son only, typically from November to February, to avoid the northeast monsoon [71]. Fishing
activity is less intense during the wet season (Lee S.F., personal communication, January 25,
2017) and so we focused on estimating dugong bycatch risk between March and November. In
Fig 3. Inventory of environmental, biological, and fisheries data summarized by site, year, and seasonfor each field site. Detailed metadata is available in S1
Table in S1 Data of the supporting information.
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KGBR, an annual composite was used to identify spatial patterns of bycatch risk, as SSF activi-
ties remain relatively constant throughout the year in Kien Giang, Vietnam.
2.2.3. Fishing extent and intensity. To map fishing intensity by gear type, we used kernel
density estimation (KDE), an interpolation technique available in QGIS for mapping hot spots
that estimates location, spatial extent, and intensity of fishing activity [72]. This non-paramet-
ric kernel method uses the probability density function of a random variable (in this case, fish-
ing gear incidence) and fits a smoothly tapered surface to each point [73]. A limited search
distance parameter (1 or 2 km) was applied based on the distance between each boat or aerial
line transect to create a continuous surface that represented the relative magnitude of fishing
intensity by gear type over the entire area of interest (S11 Fig in S1 Data). KDE was applied in
two Malaysian field sites to analyze the gear occurrence data, collected as individual point loca-
tions. In KGBR, we compiled map layers representing fishing grounds based on areas previ-
ously identified during fisher interviews and by provincial government staff. Due to limited
sightings of fishing activities in southern Vietnam, the “intensity of gear use” exposure crite-
rion was omitted from the risk equation.
2.3. Assess and visualize bycatch risk and data uncertainty
To assess risk of bycatch in each site, we combined geospatial layers of (1) species distribution,
based on habitat suitability, and (2) fisheries presence, organized by gear type, distribution,
and intensity of use (Fig 4A). The core functionality of the ByRA tool–to draw on assembled
scores of interaction rates and assess bycatch risk (Fig 4B and 4C)–is executed through the
user interface of InVEST, a freely downloadable software suite (naturalcapitalproject.stanford.
edu/software/invest) [74]. Here, we adapted the exposure-consequence criteria for habitat risk
assessment [47,75], where risk of fisheries bycatch is calculated as a function of the likelihood
of exposure (interaction between the marine mammal and the fishery), and its consequence,
which is the gear-specific impact to a species. For two additional exposure criteria unique to
bycatch risk, we defined: (1) likelihood of interaction, as the probability that the animal will
encounter a fishing gear if spatial overlap was detected, and (2) catchability, as the likelihood
of animal capture by a gear type when this overlap occurs. Similar to Samhouri and Levin [43],
species-only consequence attributes were defined as: (a) the resilience of a species to a stressor
(based on age of maturity, reproductive strategy, population connectivity, local status of spe-
cies) and (b) its sensitivity (mortality and life stages affected by gear).
2.3.1. Expert evaluation. Species-gear interactions for a total of twelve bycatch exposure
and consequence criteria were scored as guided by field observations, literature review, and,
subsequently, expert opinion (Table 3). In August 2017, researchers and agency personnel spe-
cializing in marine biology, fisheries ecology, marine veterinary medicine, biogeography, and
social sciences participated in a judgment process to score interactions (1 to 3, low to high con-
tribution to risk) along with their confidence in each opinion. Representatives from each field
site, with working knowledge of marine mammals and SSF activities, set the final rating scores.
The supplementary information lists individual exposure (E) and consequence (C) criteria,
along with justifications for the interaction ratings, data quality and attribute weights (S5 and
S6 Tables in S1 Data).
2.3.2. Spatially explicit criteria. When available GIS data could be used to characterize
species-gear interaction rates, spatially explicit criteria (SEC) layers were created to differenti-
ate rating scores over space (1 to 3, lowest to highest exposure or consequence, Fig 4). For this
application of ByRA we mapped and scored interaction rates for three exposure criteria: (i)
intensity of gear use, (ii) current status of management, and (iii) likelihood of interaction
between the gear and species (Table 3, S11, S12 and S13 Figs in S1 Data). A geospatial workflow
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Fig 4. Bycatch risk assessment conceptual model and tool process diagram. Top panel depicts how layers and rating scores are assembled and combined.
Bottom panel shows tool interface and steps to estimate risk for each grid cell within an area of interest. Colored bands in the risk plot are numerically
determined and based on the range of exposure and consequence scores (0, 1, 2 and 3 in this assessment).
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was coded as a plugin for QGIS (available at mmbycatchtoolbox.org) to automate the neces-
sary GIS operations (i.e. unions and definition queries) for preparing ByRA SEC input layers.
Each output was reclassified (1 to 3; low to high) using a Jenks natural breaks algorithm to
minimize the variation within each class. Encounter rates, or ‘likelihood of interaction’, were
calculated as the sum of overlapping layers for habitat suitability (1–3) and gear intensity (1–
3), where a sum total of 6 or 5 = high, 4 = medium, and 3 or 2 = low likelihood of interaction
between gear and species. Current status of management was scored as “1” if implemented
and “2” if identified for a given area. Areas where no management or regulation was identified
were scored “3”, which was the maximum score (highest contribution to exposure) for this
criterion.
2.3.3. Measuring bycatch risk. Two common methods for measuring environmental risk
based on expert opinion are Euclidean distance and multiplicative functions. Cumulative
Table 3. Definitions and scoring bins for the exposure and consequence criteria and Spatially Explicit Criteria (SEC).
Criteria High risk (3) Medium risk (2) Low risk (1) Description
Exposure (likelihood) criteria
Spatial overlap >30% of species overlaps
with gear
10–30% of species overlaps
with gear
<10% of species overlaps
with gear
The overlap by grid cell between the
distribution in space of each species and
gear is calculated by the toolbox.
Temporal overlap all year (12 months) most of year (4–11 mo.) occasional (1–3 mo.) The duration of time that the species and
gear overlap in space.
Intensity of gear use high intensity medium intensity low intensity Overlap between gear-type density and
species distribution. (SEC)
Likelihood of interaction
between gear and species
high likelihood medium likelihood low likelihood The overlap between habitat suitability and
intensity of gear use. The resulting
encounter rates are ranked low to high.
(SEC)
Likelihood of capture by gear high likelihood medium likelihood low likelihood The “catchability” of species by gear
includes behavior of animal during
interaction, for example, dugong may roll
around nets.
Current status of
management
no strategies identified /
implemented
management strategies
identified, not implemented
management strategies
identified & implemented
Management strategies can limit the use of
certain gears in certain areas, thereby
mitigating negative impacts to species.
(SEC)
Consequence (impact) criteria–sensitivity
Mortality lethal sub-lethal negligible The severity (direct effect) of gear on
mortality rate of a species.
Life stages affected by gear adults only mixed juveniles only If a gear strands a species before they have
the opportunity to reproduce, recovery is
likely to be inhibited.
Consequence (impact) criteria–resilience
Age of maturity >4 years 2–4 years <2 years Greater age at maturity corresponds to
lower productivity.
Reproductive strategy long calving interval /
high parental invest
medium calving interval /
high parental invest
short calving interval / med
parental invest
The extent to which a species protects and
nourishes its offspring.
Population connectivity
(DPS = distinct population
segment; ESU = evolutionary
significant unit)
negligible exchange
between the focal regional
population and other
populations
occasional movement/
exchange between the focal
regional population and
other populations
regular movement/
exchange between the focal
regional population and
other populations
The realized exchange with other
populations based on spatial patchiness of
distribution, degree of isolation, and
potential dispersal capability; based on
monitoring surveys or direct tracking
estimates. 3 = DPS or ESU; 1 = not a DPS
or ESU
Local status of the species endangered threatened or of concern low concern The conservation status of the species in-
country.
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impact mapping studies tend to use a multiplicative approach [76,77], whereas species risk
assessments typically estimate risk as the Euclidean (straight-line) distance for each species-
threat combination in risk plots [43,47], which leads to a more precautionary scoring and
higher risk [39,41]. A recent evaluation of qualitative risk assessment frameworks suggests bet-
ter model performance using a Euclidean distance measure [78]. Therefore, we selected
Euclidean distance, from the origin (minimum score) to the average of criteria scores for expo-
sure (E) and consequence (C), to quantify bycatch risk (Eq 1). If a stressor and species did not
overlap, the tool assumed that E= 0, C= 0, and therefore Risk (R
ij
) = 0 for the grid cell being
evaluated.
Rij ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðE1Þ2þ ðC1Þ2
qð1Þ
2.3.4. Characterizing uncertainty of data sources. We applied a variable weighting struc-
ture and data quality scores–i.e., a weighted-average where d
i
= data quality weight and w
i
=
attribute weight–to account for data uncertainty and substantiate the species-gear interaction
ratings for each site (Eq 2, S7 Table in S1 Data). To characterize data input uncertainty for sta-
kedholder outreach, we designed a simple tri-color matrix (Table 4). ByRA outputs coupled
with a visualization of data quality were shared with managers to convey how existing infor-
mation in data-limited sites could be used to further improve the quality of risk estimates over
time.
E¼Pn
i¼1
ei
diwi
Pn
i¼1
1
diwi
C¼Pn
i¼1
ci
diwi
Pn
i¼1
1
diwið2Þ
3. Results
ByRA generated accessible, non-technical maps for visualizing bycatch risk estimates. Map
outputs captured spatial trends in species distribution and fishing effort to highlight fishing
areas likely to have high interaction rates as well as seasonal changes in bycatch risk. The
Table 4. Diagnostic to characterize data uncertainty based on where existing information fits along a spectrum of green-yellow-red (highest to lowest data quality,
respectively).
Data type Green Yellow Red
Animal sightings
distribution
Data collected during line transect survey and could be
used to estimate relative abundance with robust
methodologies and measurements of uncertainties.
Sightings/photo id collected during
opportunistic surveys; relative abundance
estimation might be possible.
Very few sightings collected during line
transect or opportunistic survey; no
formal abundance estimation possible.
Habitat
suitability
Estimated from modeling; quantification of
uncertainty available from modeling and collection of
environmental variables.
Estimated using non-modeled distribution
methodology; minimal environmental
variables collected.
Information from other regions used to
estimate animal distribution.
Fishing effort /
gear type
densities
Data available such as fishing effort per unit of distance
or time possible using modeling; uncertainty
measurements possible.
Spatial distribution of fishing gears, relative
(to time or space) fishing effort or fishing
gears, based on interviews or expert opinion.
Sparse or incomplete data; no geospatial
or precise localization of the fishing
effort/gear distribution.
Bycatch /
stranding data
Robust data about bycatch available from interviews,
boat survey, or stranding records; estimation of
bycatch rate possible along with measurement of
uncertainties.
Relative estimation of bycatch from interviews
or stranding data.
No estimation of bycatch or strandings
available.
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uncertainty of data inputs was characterized and outputs were error checked and improved by
local experts and stakeholders.
3.1. Visualizing bycatch risk
We found that risk estimates in ByRA were driven primarily by the fishing method (gear type)
and the density of fishing activities that were found to overlap suitable marine mammal habitat
areas (“intensity of use” and “likelihood of interaction” exposure criteria, respectively). Areas
with high marine mammal occurrence and fisheries activity were predicted as highest risk of
SSF bycatch. Specific to the three field sites, areas where nets and trawls were used (gears rated
as highest likelihood and impact on a number of exposure and consequence attributes by local
experts and the literature, S5 and S6 Tables in S1 Data) were identified by the tool to pose sub-
stantial risk to both Irrawaddy dolphins and dugongs. By season and scenario, a range of
bycatch risk maps were produced–classified as lowest, intermediate and highest risk–the latter
of which served to pinpoint areas of greatest bycatch concern (Fig 5).
Visualization of risk outputs also identified drivers of risk by gear type and subregion (Fig
6). Nets and trawls were scored by local experts to have a considerably higher likelihood (expo-
sure) and impact (consequence) to both marine mammal species where they co-occurred,
while pots and traps were more benign, especially for dugongs. The top-right corner of ByRA
risk plots (darkest blue color bands in Fig 6) indicated which gears were the strongest drivers
of bycatch risk, when each species-gear interaction occurred. This included nets for dugongs
in SBTI, nets and pots and traps for Irrawaddy dolphins in KUCG, and additionally trawls for
Irrawaddy dolphins in KGBR. If these interactions were to occur in areas of highest suitability
for marine mammals, the estimated risk increased further (rightward movement along the x-
axis) due to a greater likelihood of species-gear interaction and, therefore, higher average expo-
sure score. Variation in bycatch exposure over space and time was captured as gear-specific
exposure ratings, and then reflected as a subset of spatially explicit criteria input layers (Fig 6,
S11-13 Figs in S1 Data). Separately, we shared SEC layers in a simple visual format (maps and
tables) with marine mammal scientists and managers to facilitate discussions about data
uncertainty and validate preliminary findings.
3.2. Capturing expert knowledge
ByRA’s map input layers and species-gear interaction scores were iteratively improved through
expert review and feedback using interactive maps and discourse in a participatory GIS frame-
work. A combination of regional meetings, workshops, and expert interviews served to refine
the approach and confirm early results. A transparent and flexible approach to stakeholder
involvement and risk assessment was cited as key by our collaborators to build trust in the pro-
cess and elicit local knowledge often buried in reports and on the data hard drives of represen-
tatives from provincial governments and other institutions. For example, colleagues based in
Vietnam, who were not able to attend a meeting with our team, later provided key data layers
on the location of river mouths for modeling Irrawaddy dolphin habitat suitability and com-
mon gears observed by government officials in fishing areas where GPS use was prohibited.
Two site visits in 2017 served to build a shared understanding of the ByRA approach
among the project team and how ByRA could be further standardized to accommodate vary-
ing quantities and qualities of data and fill critical information gaps,. The second site visit was
a ByRA learning exchange workshop with our in-country collaborators to demonstrate how
they could apply the tool in their home countries. We found that including these face-to-face
meetings in our project budget was a necessary step to galvanize action across the region in
support of research efforts, specifically, to make better use of existing data from animal and
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Fig 5. Estimated bycatch risk in three field sites (A-C). (A) Sibu-Tinggi Islands, Johor, Malaysia (SBTI) for dugongs, (B) Kuching Bay,
Malaysia (KUCG) during the dry season, pre-monsoon and post-monsoon, B1-3 respectively, and (C) Kien Giang Biosphere Reserve, Vietnam
(KGBR) for Irrawaddy dolphins. Data quality levels of four categories of ByRAinputs from Tables 4and 5are displayed as colored diamonds.
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Fig 6. Plots and bar charts summarizing drivers and emergent patterns of bycatch risk. Coordinates (greysymbols) mapped as the weighted average of exposure and
consequence criteria scores. They explain the contribution to risk of each gear category by subregion, habitat suitability type, and scenario. ByRA calculates risk based on
distance from the origin (exposure = 1.0, consequence = 1.0) to each coordinate on a cell-by-cell basis where darker blue indicates higher risk. Star symbol with yellow
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SSF observation records, expert knowledge, government reports, and the literature. These con-
nections also made subsequent engagement and remote support for applying the ByRA in new
geographies easier because many scientific and technological hurdles (e.g., capacity strength-
ening, understanding the methodological approach, user interface and data uncertainty) had
been overcome.
3.3. Characterizing uncertainty
In-person meetings with stakeholders in August 2017 yielded a diagnostic for data uncertainty,
showing a gradient of data input quality across the three field sites (Table 5). This was used by
our team to identify and discuss commonalities across locations and taxa, prioritize new tech-
niques to undertake such as local surveys of environmental data, animal occurrence, fisheries
effort, and acknowledge uncertainty as part of outreach. We found that by visualizing uncer-
tainty of data inputs qualitatively (using green, yellow and red colors of a globally-recognized
traffic light signal), scientists and managers had a clearer set of priorities for future acquisition
and integration of existing information; with an emphasis on filling data gaps, reducing data
uncertainty in areas of highest bycatch concern identified by the tool, and restarting monitor-
ing activities that had stalled due to funding limitations.
ByRA map outputs were also error checked by in-country collaborators with certain areas
flagged as potential over- or underestimates of fishing activity and habitat suitability, two impor-
tant drivers of bycatch risk in the tool. We could only characterize data uncertainty and validate
outputs because local experts were present to corroborate bycatch risk estimates based on infor-
mation previously provided by researchers and knowledge holders. The visualization of uncer-
tainty across a range of data inputs and field sites assisted researchers in Malaysia, Vietnam, and
Cambodia to chart a path forward by concentrating limited resources in areas with substantial
information gaps and consider monitoring protocols and technologies that had already yielded
substantial returns in neighboring regions (see Vu et al. [57] for KGBR Vietnam example).
3.4. Emergent patterns and findings
For all three SEA field sites, ByRA outputs identified emergent spatial trends of interactions
between SSF and marine mammals; specifically, fishing gears and locations that are likely to
have high bycatch rates and damaging effects on marine mammal population viability. For
instance, coastal areas with the highest level of bycatch risk to dugongs (darkest blue bars in
Fig 6A) were well distributed across the four subregions of SBTI (between 13–44 km
2
) despite
halo at the top-right indicates conditions of highest risk. Bar charts show total area at risk (x-axis) by risk level (blue color palette) and subregion (y-axis) for each field
site (A-C) and scenario. Note the two x-axes for KUCG site in panel B.
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Table 5. Classification of data input uncertainty in three SEA field sites. Green = substantial data available, yellow = limited data available, and red = data are either
incomplete or severely limited.
Data type Field sites
SIBU KUCG KGBR
Animal sightings
distribution
Systematic transect aerial survey Systematic transect boat survey Systematic line transect boat survey; not enough
sightings to characterize distribution
Habitat suitability Seagrass data and mammal acoustics; limited
environmental data collected during survey
Environmental data collected with
the transect survey
Environmental data partially collected
Fishing effort / gear
type densities
Collected during line transect surveys and from interviews From interviews only
Bycatch / stranding
data
From interviews and some records of stranded
animals due to interactions with fisheries
Presence/absence of bycatch from interviews only
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subregion 4 being the most expansive (68% of combined total area of subregions 1–3), closest
to the main fishing village (Mersing), and least protected (no fisheries management identified).
ByRA revealed this and other non-obvious patterns that would be difficult to uncover without
a spatio-temporally explicit risk assessment framework. For instance, risk coordinates plotted
for each species-gear interaction and range of habitat suitability highlighted that nets deployed
in some areas of SBTI posed an intermediate level of bycatch risk, similar to other SSF gears.
On the other hand, risk from nets was highest inside subregion 3 (Figs 5A and 6A). Further-
more, area summaries of bycatch risk levels as bar charts (Fig 6) displayed how risk shrinks
and expands over space and time, including seasonal changes in total area of estuarine and
coastal waters at greatest SSF bycatch risk.
As illustrated in the Kuching Bay (KUCG) field site, seasonal snapshots depicted how
bycatch risk is likely to change during the year and within subregions (Fig 5B). For the dry sea-
son (May to September) the greatest proportion of intermediate-highest risk, relative to total
area, was inside the river system of subregions 3 and 4 (37 and 70%, respectively) compared to
just 2 and 5% in coastal subregions. After the wet season, additional risk hotspots emerged in
coastal areas of subregions 1 and 2 (Fig 6B) because SSF activities increased and Irrawaddy
dolphin occurrence was high in these areas. Distance to land was identified by Maxent to be
the most important environmental variable for Irrawaddy dolphins post-monsoon (50% over-
all contribution; S4 Table in S1 Data). As a result, bycatch risk estimates shifted to their highest
levels in these coastal areas of KUCG until the dry season, specifically where there was high
SSF occurrence and no fisheries management strategies identified.
The most data-scarce of the three SEA sites, KGBR Vietnam represents a template for
ByRA users applying the tool in places where existing data are severely limited (red to yellow
uncertainty levels in Table 4). Despite relying entirely on data collected by others who used
indirect measures of SSF activity and marine mammal distribution (i.e. fisher interviews, par-
ticipatory mapping, and environmental overlays such as distance to land, depth and other opti-
mal habitat variable ranges), we found three distinct areas within subregions 1, 5 and 6 that
accounted for almost all (88%) of the highest level of bycatch risk across the KGBR site. There-
fore, it was still possible to identify specific locations in southern Vietnam–i.e. greatest likeli-
hood of interaction between dolphins and high-impact gears (nets and trawls, in this case)–as
priority candidates for further monitoring and data collection.
4. Discussion
In this study, we present spatially explicit estimates of bycatch risk in three SE Asian field sites.
A total of 10 810 km
2
of estuarine and coastal waters were systematically screened to home in
on 805 km
2
(approximately 7.5% of the total area of interest) identified as highest level of
bycatch risk to dugongs and dolphins. For these areas of concern, nets and trawls were the
gear types associated with the highest bycatch risks in large part due to greater exposure (distri-
bution and intensity relative to other SSF gears) and consequences (mortality) when these fish-
eries encounter marine mammals. The spatially and temporally explicit scenarios in Kuching
Bay showed patterns of risk that shifted to and from the estuarine and coastal waters across
seasons. By integrating information from fisher interviews and line-transect surveys, we
mapped the likelihood of species-gear interactions over space and time and at local scales. In
parallel, geospatial analysis techniques such as participatory mapping with local scientists and
agency experts were used to build habitat suitability layers by site and season. Finally, SSF gear
and species interaction rates were scored to assess and map bycatch risk and data uncertainty.
These ByRA outputs demonstrated the potential of a new tool to co-create knowledge and gar-
ner insights about bycatch exposure in data-limited small-scale fisheries.
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Characterizing the effects of marine mammal bycatch through space and time can be com-
pared to finding a needle in a haystack–as bycatch is difficult to observe and quantify [18,79].
However, the results from ByRA are encouraging. Despite the challenges to sustainability that
small-scale fisheries face, ByRA demonstrates the ability to make use of limited data as a
means of addressing some of these challenges, e.g., pinpointing fishing areas where and when
to concentrate efforts (monitoring, education, and outreach) and, conversely, identifying low
risk areas where additional effort is not warranted, saving time and resources. This kind of
information is urgently needed in Southeast Asia, and many parts of the developing world,
where resources and capacity to conserve marine biodiversity and mitigate bycatch are scarce
[6,25]. This can also help fisheries in developing countries comply with new import regula-
tions from provisions within the Marine Mammal Protection Act [14,15].
Against a backdrop of the importance of SSF as nutrition source and livelihood for coastal
communities [3,80], effective bycatch mitigation depends first on identifying emergent pat-
terns of exposure, which is driven by myriad factors including prey abundance, seasonality,
and gear preferences [36,81,82]. Nevertheless, outputs from ByRA identified patterns in
bycatch occurrence (e.g., particular species, fishing gears, and locations) that had high interac-
tion rates. For instance, in subregion 3 of SBTI, interactions between nets and dugongs were
numerically determined to be the highest risk level (2.79 for exposure and 2.80 for conse-
quence; max score of 3.0) of all species-gear interactions evaluated. Interestingly, the bycatch
exposure score was highest for dolphins in KUCG with nets deployed during the pre-monsoon
in subregion 4 (2.71 out of 3.0), which aligns with evidence that gillnets are an acute threat
causing direct mortalities to marine mammals in significant numbers worldwide [18,19]. By
disentangling this and other drivers of exposure, we found patterns of highest bycatch risk
under conditions of (1) high species-fishery encounter rates, (2) high-impact gears in use
(especially nets and trawls) (3) no management identified, and (4) suitable marine mammal
habitat.
4.1. Limitations and simplifications
An obvious information gap in our study was bycatch data from onboard observers, a com-
mon requirement in European and US fisheries policy [83,84], and a monitoring technique in
SEA that is not commonly utilized. To date, efforts to characterize bycatch and map risk in
Southeast Asia have relied almost entirely on fisher interviews to map the extent of fisheries
operating in the region [48,85]. Without technology to comprehensively monitor use of the
marine environment by fishers, we were unable to capture activities or interactions that occur
at night or with discarded or unattended gear. The use of onboard observers, remote electronic
monitoring (REM), and other rapid, low-cost technologies [85], would greatly enhance the
ability of ByRA to identify high-risk, under-surveyed areas. However, one of the strengths of
the ByRA approach is the ability to accurately described and account for data uncertainty, a
clear demonstration of how even low-resolution information can be applied to more effectively
investigate risk and guide future bycatch monitoring and management [79,86]. For example,
areas of highest bycatch risk in the SBTI field site provide more evidence that supports recent
calls for designation of a dugong sanctuary inside the Mersing Archipelago [56]. Likewise,
despite a paucity of data in KGBR, habitat suitability maps derived entirely from GIS overlays
showed strong agreement with Irrawaddy dolphin sightings acquired independently of the
ByRA analysis (S4 Fig in S1 Data) [58,87]. Although these analyses provide more information
and insight on bycatch risk areas and management interventions that are likely to reduce risk,
additional research is needed to compare modeled outputs to other modeled empirical data on
bycatch rates, or strandings [88,89].
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Another consideration for future research is the need to account for antagonistic or syner-
gistic effects, that may better reflect the total risk of bycatch to the species [78]. While ByRA’s
default calculation of cumulative risk is additive (sum of individual risk scores for all gear
types evaluated), intermediate outputs can be reanalyzed and combined as appropriate in each
decision context. There is also a need for better data on the interaction rates of species with
SSF gears such as gillnets and traps that also have ecological impacts to habitats and ecosystems
[36,82]. We took a precautionary approach of maximum risk based on experts opinion (as in
[39,41,81]), due to the strong evidence that associates SSF gear with marine mammals strand-
ing and mortality [21,22]. Data uncertainty with fishing locations are also an important con-
sideration. Our kernel density estimates were limited by a survey line artifact (circular
horseshoe pattern), that when reclassified into three levels of exposure gave the effect of dis-
continuity in the fishing effort and gear-type intensity maps (S11 and S13 Figs in S1 Data).
Data uncertainty levels yellow and red (adequate to limited data quality) served to flag this and
other areas for improvement (Table 5).
4.2. Future directions
Marine mammal scientists and conservation groups in the SEA region continue to collect data
on environmental habitat variables to understand seasonality, cetacean behaviors, and enhance
the effectiveness of protection and management measures [33,56]. This information is critical,
especially to reduce bycatch risk. These data will inform habitat model selection [32,57,90]
and increase ByRA’s analytical complexity [79] in support of more dynamic ocean manage-
ment [35]. For example, marine megafauna networks in Vietnam and Cambodia aim to fill
information gaps over the next few years, e.g., additional sighting records of Irrawaddy dol-
phins for correlative habitat models such as Maxent, while integrating local knowledge and
strengthening capacity to generate actionable information for communicating with govern-
ment officials and policy makers over the longer term [58]. These efforts may be more feasible
in areas identified by the tool as highest relative risk (e.g., subregions 1–2 and 5–6 in KGBR)
and where there is likely to be interest in the conservation of important marine mammals for
tourism and alternative livelihoods [4,5,80]. Deployment of other technologies such as passive
acoustic monitoring and telemetry can also aid in these efforts.
Species-gear interactions and their impacts vary widely by location and across small-scale
fisheries [25,36,82], which underscores the importance of ByRA as a tool to integrate existing
knowledge, characterize bycatch likelihood and identify areas where bycatch risk is high. We
found that a substantial investment in the process of scoring species-gear interactions (expo-
sure and consequence criteria) based on available field data, literature, and expert knowledge
was essential to capture salient effects associated with small-scale fisheries gears and other local
fishing methods. In the SEA case studies, consequence criteria scores had a limited range
because only one species was evaluated in each site. Still, variation in the final weighted average
of exposure criteria scores highlight how much risk estimates posed by one gear can vary over
space and time (e.g., 1.71 to 2.81 range of exposure scores for nets within SBTI). Spatial plan-
ners and managers can benefit from this insight by mapping fishing gears and at-risk marine
species [46,65,91] and then applying ByRA to identify bycatch hotspots where mitigation is
needed to reduce bycatch risk.
There are also opportunities to apply ByRA for multispecies assessment, which can illumi-
nate high risk gears across species by season or scenario. This may include comparing risk
between fishing areas, how different gears contribute to risk, or evaluating alternative manage-
ment strategies under consideration [43,47,92]. Through leveraging global systems and
regional seafood ratings programs that compile small-scale fisheries knowledge [9,93], multi-
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species risk assessment can be applied to disentangle the human dimension of fisheries bycatch
and integrate locally-relevant criteria, such as set height or mesh strength of nets, that embrace
the conceptual complexity of marine megafauna conservation research [79]. After risk base-
lines have been developed [94,95], it is possible to compare feasible management and policy
interventions. Finally, GIS-based scenarios that capture inter-annual variability and modifica-
tions of fishing gears [52] could be incorporated into ByRA to examine how the location and
timing of risk is likely to change in the future, and anticipate at-risk areas in need of further
monitoring and evaluation.
4.3. Conclusion
We created a spatially explicit management tool (ByRA) to better understand and characterize
risk of bycatch posed by common fishing gears in data-limited small-scale fisheries. Three
unique field sites, where substantial marine mammal bycatch has been reported, were system-
atically screened using existing data and a powerful form of visualization to map areas and sea-
sons of concern in a region where distinct spatio-temporal patterns of bycatch risk had not
been identified. ByRA employed a range of geospatial and participatory engagement tech-
niques–including specific methods tailored for data scarce areas–to compile and analyze exist-
ing information about small-scale fisheries and better plan further research, bycatch
mitigation, and species recovery and protection. This information enables managers to estab-
lish baselines, deliberate with stakeholders on the next steps for data acquisition, and identify
interventions that are likely to mitigate bycatch risk in small-scale fisheries. It may also help
these fisheries comply with European Commission and U.S. regulations [84,96] that require
efforts to reduce the acute threat of marine mammal bycatch to sustainable levels.
Supporting information
S1 Data.
(DOCX)
Acknowledgments
We thank the MareCet Research Organization, Universiti Malaysia Sarawak, and Kien Giang
Biosphere Reserve, Vietnam for local knowledge, insight and connections to stakeholders, Lee
S. F., W. Laovechprasit, T.A. Tho, T. Huynh, D.H. Minh, G. Minton, M. Czapanskiy, J. Rude-
busch, S. Fairchild, R. Anderson, P. Ferber, A. Haissoune, T. Collombat, and S. Tubbs for sci-
entific and technical support; D. Briscoe for Maxent modeling assistance; and S. McDonald, R.
Pelc, and J. Silver for ByRA design input.
Author Contributions
Conceptualization: Gregory M. Verutes, Andrew F. Johnson, Louisa S. Ponnampalam,
Rebecca L. Lewison, Ellen M. Hines.
Data curation: Gregory M. Verutes, Andrew F. Johnson, Marjolaine Caillat, Louisa S. Pon-
nampalam, Cindy Peter, Long Vu, Chalatip Junchompoo, Ellen M. Hines.
Formal analysis: Gregory M. Verutes, Marjolaine Caillat, Ellen M. Hines.
Funding acquisition: Louisa S. Ponnampalam, Rebecca L. Lewison, Ellen M. Hines.
Investigation: Gregory M. Verutes, Andrew F. Johnson, Louisa S. Ponnampalam, Cindy
Peter, Long Vu, Chalatip Junchompoo, Rebecca L. Lewison, Ellen M. Hines.
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Methodology: Gregory M. Verutes, Andrew F. Johnson, Marjolaine Caillat, Louisa S. Pon-
nampalam, Cindy Peter, Long Vu, Rebecca L. Lewison, Ellen M. Hines.
Project administration: Louisa S. Ponnampalam, Rebecca L. Lewison, Ellen M. Hines.
Resources: Gregory M. Verutes, Louisa S. Ponnampalam, Cindy Peter, Long Vu, Chalatip
Junchompoo, Ellen M. Hines.
Software: Gregory M. Verutes, Ellen M. Hines.
Supervision: Gregory M. Verutes, Louisa S. Ponnampalam, Cindy Peter, Long Vu, Chalatip
Junchompoo, Rebecca L. Lewison, Ellen M. Hines.
Validation: Gregory M. Verutes, Louisa S. Ponnampalam, Cindy Peter, Ellen M. Hines.
Visualization: Gregory M. Verutes, Ellen M. Hines.
Writing – original draft: Gregory M. Verutes, Andrew F. Johnson, Marjolaine Caillat, Louisa
S. Ponnampalam, Cindy Peter, Rebecca L. Lewison, Ellen M. Hines.
Writing – review & editing: Gregory M. Verutes, Andrew F. Johnson, Louisa S. Ponnampa-
lam, Cindy Peter, Long Vu, Chalatip Junchompoo, Rebecca L. Lewison, Ellen M. Hines.
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Marine mammal bycatch risk assessment in data-limited fisheries
PLOS ONE | https://doi.org/10.1371/journal.pone.0237835 August 20, 2020 25 / 25
... Megafauna bycatch is a high conservation concern for which there is often inadequate data (Figueiredo et al., 2020;Mannocci et al., 2020). Specifically, in data-poor regions, accessing data required for assessments may be difficult due to the natural complexities of fisheries, especially among artisanal or smallscale fisheries (SSF) (FAO, 2020;Verutes et al., 2020). SSF's are broadly defined as smaller vessels with lesser tonnage, that largely use manual labor as opposed to mechanical equipment, and fish predominately in neritic waters. ...
... Risk assessments are more likely to reflect on-the-ground conditions if coastal community members are actively involved in the discussion and implementation of the risk assessment process (Campbell and Cornwell, 2008;Sawchuk et al., 2015;Visalli et al., 2020). The Bycatch Risk Assessment (ByRA) (Figure 1), a spatially explicit analysis that can integrate PGIS data collection methods, was first tested in several southeastern Asian fisheries (Hines et al., 2020;Verutes et al., 2020). The ByRA model offers a structural framework specifically for assessing bycatch in data-poor fisheries by making use of available information and incorporating expert opinion and local stakeholder input via fisher interviews to guide place-based management recommendations for reducing bycatch. ...
... Fisher participation was prevalent in Step 1, 4, and 5 for data collection, data review, and output presentation and discussions. See Verutes et al. (2020) for further description on ByRA model including how ratings scores are assembled, and risk plots are determined. If a gear strands a species before they have the opportunity to reproduce, recovery is likely to be inhibited. ...
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Uncertainties about the magnitude of bycatch in poorly assessed fisheries impede effective conservation management. In northern Peru, small-scale fisheries (SSF) bycatch negatively impacts marine megafauna populations and the livelihoods of fishers which is further elevated by the under-reporting of incidents. Within the last decade, accounts of entangled humpback whales (HBW) ( Megaptera novaeangliae ) off the northern coast of Peru have increased, while Eastern Pacific leatherback turtles (LBT) ( Dermochelys coriacea ) have seen over a 90% decline in nesting populations related in large part to bycatch mortality. By leveraging the experience and knowledge of local fishers, our research objectives were to use a low-cost public participation mapping approach to provide a spatio-temporal assessment of bycatch risk for HBW and LBT off two Peruvian fishing ports. We used an open-source, geographic information systems (GIS) model, the Bycatch Risk Assessment (ByRA), as our platform. Broadly, ByRA identifies high bycatch risk areas by estimating the intersection of fishing areas (i.e., stressors) with species habitat and evaluating the exposure and consequence of possible interaction between the two. ByRA outputs provided risk maps and gear risk percentages categorized as high, medium, and low for the study area and seven subzones for HBW in the austral winter and LBT in the austral summer. Overall, the highest bycatch risk for both species was identified within gillnet fisheries near the coast. Bycatch risk for most gear types decreased with distance from the coast. When we separated the ByRA model by port, our map outputs indicate that bycatch management should be port specific, following seasonal and spatial variations for HBW, and specific fishing gear impacts for HBW and LBT. Combined with direct bycatch mitigation techniques, ByRA can be a supportive and informative tool for addressing specific bycatch threats and marine megafauna conservation goals. ByRA supports a participatory framework offering rapid visual information via risk maps and replicable methods for areas with limited resources and data on fisheries and species habitat.
... For applications that produce several habitat-selection models in one area, the environmental variables found to be significant predictors of distribution often differ between species (Garaffo et al., 2011), providing baseline knowledge on subtle habitat characteristics of sympatric cetacean species (Tobeña et al., 2016;Kuit et al., 2019). There are fewer examples, however, of multiple SDMs developed for one species that illuminate seasonal patterns of distribution, density, or behavior (Daura-Jorge et al., 2005;Campbell et al., 2015;Verutes et al., 2020). ...
... While some occurrence localities were removed from the prediction, one key advantage of this classification method was to allow for standardization across multiple SDMs of the same species (Merow et al., 2013). As with three seasonal SDMs developed for Irrawaddy dolphins in Kuching Bay, Malaysia (Verutes et al., 2020), all locations with an ROR below the 10% ROR threshold were classified as limited or unsuitable habitat. The remaining cells where classified according to three levels of habitat suitability: (1) lowest, ranging from 5 to 10%; ...
... Evidence is mounting that illegal fishing activities, especially bottom-trawling, are ubiquitous to the Vietnam-Cambodia border region (Vu et al., 2017;Böhm, 2019;Reid et al., 2019). Addressing the vast monitoring deficiencies of the Southeast Asia region (Teh et al., 2015;Hines et al., 2020) is a necessary first step to understand where and when IUU fishing impacts, such as bycatch and habitat destruction, are most consequential to Irrawaddy dolphins and other marine mammals (Read, 2008;Jackson-Ricketts et al., 2020;Verutes et al., 2020). Moving forward, systematic surveys of the Kep Archipelago are needed to challenge our assumptions related to seasonal variability of the marine environment and fill gaps of earth observation data layers near shorelines and river mouths. ...
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Fishing activities continue to decimate populations of marine mammals, fish, and their habitats in the coastal waters of the Kep Archipelago, a cluster of tropical islands on the Cambodia-Vietnam border. In 2019, the area was recognized as an Important Marine Mammal Area, largely owing to the significant presence of Irrawaddy dolphins (Orcaella brevirostris). Understanding habitat preferences and distribution aids in the identification of areas to target for monitoring and conservation, which is particularly challenging in data-limited nations of Southeast Asia. Here, we test the hypothesis that accurate seasonal habitat models, relying on environmental data and species occurrences alone, can be used to describe the ecological processes governing abundance for the resident dolphin population of the Kep Archipelago, Cambodia. Leveraging two years of species and oceanographic data-depth, slope, distance to shore and rivers, sea surface temperature, and chlorophyll-a concentration-we built temporally stratified models to estimate distribution and infer seasonal habitat importance. Overall, Irrawaddy dolphins of Kep displayed habitat preferences similar to other populations, and were predominately encountered in three situations: (1) water depths ranging from 3.0 to 5.3 m, (2) surface water temperatures of 27-32 • C, and (3) in close proximity to offshore islands (< 7.5 km). With respect to seasonality, statistical tests detected significant differences for all environment variables considered except seafloor slope. Four predictor sets, each with a unique combination of variables, were used to map seasonal variation in dolphin habitat suitability. Models with highest variable importance scores were water depth, pre-and during monsoon season (61-62%), and sea surface temperature, post-monsoon (71%), which suggests that greater freshwater flow during the wet season may alter primary productivity and dolphin prey abundance. Importantly, findings show the majority of areas with highest habitat suitability are not currently surveyed for dolphins and located outside Kep's Marine Fisheries Management Area. This research confirms the need to expand monitoring to new areas where high-impact fisheries and other human activities operate. Baseline knowledge on dolphin distribution can guide regional conservation efforts by taking into account the seasonality of the species and support the design of tailored management strategies that address transboundary threats to an Important Marine Mammal Area.
... Bycatch risk is evaluated based on the spatial and temporal coincidence of ranked probabilities of overlap between a species' occurrence and fishing; such analyses can be used to set priorities for collecting data on bycatch rates and fishing effort, and can identify areas deserving of management efforts and further research. Verutes et al. (2020) show an example of the use of the ByRA tool in a case study examining risk to Irrawaddy dolphins (Orcaella brevirostris) and dugongs (Dugong dugon) from five small-scale fishing gear types in Malaysia and Vietnam. ...
... Thus, interactive tools such as MMBIET allow users to explore scenarios and identify robust management strategies, provided they are used correctly. Similarly, demonstrated co-occurrence of high-risk fisheries and marine mammal populations, either through qualitative evaluation or structured methods such as GIS mapping tools (e.g., Hines et al., 2020;Verutes et al., 2020;Welch et al., 2020), can help identify priority spatial areas for bycatch reduction. Bycatch mitigation, which often takes many years to accomplish, could begin in these areas while research continues. ...
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Bycatch in marine fisheries is the leading source of human-caused mortality for marine mammals, has contributed to substantial declines of many marine mammal populations and species, and the extinction of at least one. Schemes for evaluating marine mammal bycatch largely rely on estimates of abundance and bycatch, which are needed for calculating biological reference points and for determining conservation status. However, obtaining these estimates is resource intensive and takes careful long-term planning. The need for assessments of marine mammal bycatch in fisheries is expected to increase worldwide due to the recently implemented Import Provisions of the United States Marine Mammal Protection Act. Managers and other stakeholders need reliable, standardized methods for collecting data to estimate abundance and bycatch rates. In some cases, managers will be starting with little or no data and no system in place to collect data. We outline a comprehensive framework for managing bycatch of marine mammals. We describe and provide guidance on (1) planning for an assessment of bycatch, (2) collecting appropriate data (e.g., abundance and bycatch estimates), (3) assessing bycatch and calculating reference points, and (4) using the results of the assessment to guide marine mammal bycatch reduction. We also provide a brief overview of available mitigation techniques to reduce marine mammal bycatch in various fisheries. This paper provides information for scientists and resource managers in the hope that it will lead to new or improved programs for assessing marine mammal bycatch, establishing best practices, and enhancing marine mammal conservation globally.
... Strong forms of citizen power may also include establishing official partnerships and councils (Schulz et al., 2021), although these may not be very interactive and are limited to the inclusion of a more select group of stakeholders. Depending on the topic, options for the use of geographic information systems (GIS) and mapping may be used as an interactive way to visualize and position aspects important for planning (Hull & Huijbens, 2016;Verutes et al., 2020). Planners may even opt to use gamification (Seiffert-Brockmann et al., 2018), virtual reality (Schauppenlehner et al., 2018) or citizen science (Schauppenlehner et al., 2021) methods to engage the tech-savvy stakeholders. ...
... The unintended catching of not targeted species ("bycatch") is a well-documented issue in fisheries. Examples include seabirds (Clay et al., 2019;Dias et al., 2019), marine mammals (Verutes et al., 2020), sharks (Silva and Ellis, 2019), turtles (Virgili et al., 2018), and other fish species (Hazen et al., 2018). Sea turtle entrapment in shrimping gear is a well-known example of bycatch in the United States. ...
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The “Georgia Jumper” turtle excluder device (TED) is a rare example of a well-accepted conservation tool required by regulation. Mediated by the UGA Marine Extension and Georgia Sea Grant, Georgia's shrimping industry was integral to the design, revision, and implementation of excluder devices, since the earliest “jellyball shooter” proposed to NMFS in 1980. This paper highlights fisher involvement in the creation of the popular “Georgia Jumper” TED. Both the Diffusion of Innovation and the Traditional Ecological Knowledge literatures stress the importance of meaningful engagement of user communities in the development of new management approaches, and make specific recommendations for improving uptake of new methods. Consistent with literature expectations, fisher and industry participation in the development, testing, and implementation of TEDs has been key to the general acceptance of TEDs in Georgia. This paper illustrates the importance of fisher participation in conservation efforts such as these.
... Small-scale fisheries present additional challenges with respect to bycatch monitoring, because of their spatial and operational variability, and unregulated, unstructured working environment (Hines et al., 2020). Methods such as rapid survey interviews, spatial risk assessments and monitoring catch at landing centers have provided baseline bycatch estimates for small-scale fisheries in developing countries (Pilcher et al., 2017;Temple et al., 2019;Hines et al., 2020;Verutes et al., 2020). However, fragmented monitoring e orts in small scale fisheries and the resultant inconsistencies in or the lack of bycatch data hamper the successful implementation of mitigation measures in small-scale fisheries (Gilman et al., 2010;Teh et al., 2015). ...
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Marine mammal interactions with fisheries, such as bycatch and depredation, are a common occurrence across commercial and small-scale fisheries. We conducted a systematic review to assess the management responses to marine mammal interactions with fisheries. We analyzed literature between 1995 and 2021 to measure research trends in studies on direct and indirect interactions for: (i) high and low to middle-income countries, (ii) fishery operations (commercial and small-scale), and (iii) taxonomic groups. Management responses were categorized using the framework described previously in peer-reviewed studies. Marine mammal bycatch remains a major conservation concern, followed by marine mammal depredation of fishing gear. A high proportion of studies concentrated on commercial fisheries in high-income countries, with an increase in small-scale fisheries in low to middle-income countries between 1999 and 2020. The insufficient understanding of the social dimensions of interactions and the inevitable uncertainties concerning animal and human behaviors are major challenges to effective management. Despite the key role of human behavior and socioeconomics, we found only eight articles that incorporate human dimensions in the management context. Integrating social dimensions of marine mammal interactions with fisheries could help in setting pragmatic conservation priorities based on enhanced understanding of critical knowledge gaps. An area-specific adaptive management framework could be an effective tool in reducing the risk to marine mammals from fisheries by coupling technical solutions with socio-economic and political interventions. We conclude that despite the vast body of literature on this subject, a “silver bullet” management solution to marine mammal interactions with fisheries does not yet exist.
... TPH contents of pits), this method assesses and maps the relative concentrations of potential pollutants at any point in a given area, without the need to define the proportion of contaminants mobilised (Khan et al., 2018;Zhang et al., 2018). To determine the spatial extent of a hazard, a cost-distance analysis was conducted (Kaffa et al., 2021;Udoh & Ekanem, 2011;Verutes et al., 2020). This method calculates the least accumulative cost-distance for each pixel to the nearest source over a cost surface, given a maximum distance (ESRI, 2013). ...
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