ArticlePDF Available

Getting to the bottom of bycatch, a GIS-based toolbox to assess the risk of marine mammal bycatch

  • The MareCet Research Organization

Abstract and Figures

Marine mammal bycatch poses a particular challenge in developing countries, where data to document bycatch and its effects are often lacking. Using the Bycatch Risk Assessment (ByRA) toolkit, based on InVEST open-source models, we chose 4 field sites in Southeast Asia with varying amounts of data on marine mammals and fishing occurrence: Trat province in the eastern Gulf of Thailand, the Sibu-Tinggi Islands and Kuching Bay, Malaysia, and Kien Giang Biosphere Reserve in southwestern Vietnam. These field sites have similar species of coastal marine mammals, small-scale and commercial fisheries, and support for research from universities and/or management. In Thailand and Kuching, results showed changing patterns of fishing and Irrawaddy dolphin Orcaella brevirostris habitat use across seasons, showing how bycatch risk could change throughout the year. Risk maps for dugongs Dugong dugon in peninsular Malaysia highlighted patterns of bycatch risk concentrated around a mainland fishing pier, and revealed high risk in a northern subregion. In Vietnam, first maps of bycatch risk for the Irrawaddy dolphin showed the highest risk driven by intensive use of gillnets and trawling gear. ByRA pinpointed areas of spatial and seasonal bycatch exposure, and estimated the consequence of bycatch on local species, providing managers with critical information on where to focus bycatch mitigation and meet new global standards for US Marine Mammal Protection Act and other international regulation (e.g. Official Journal of the European Union 2019; Regulation 2019/1241) compliance. The toolbox, a transferable open-source tool, can be used to guide fisheries management, marine mammal conservation, spatial planning, and further research.
Content may be subject to copyright.
Endang Species Res
Vol. 42: 37–57, 2020 Published June 4
© The authors 2020. Open Access under Creative Commons by
Attribution Licence. Use, distribution and reproduction are un -
restricted. Authors and original publication must be credited.
Publisher: Inter-Research ·
*Corresponding author:
ABSTRACT: Marine mammal bycatch poses a particular challenge in developing countries,
where data to document bycatch and its effects are often lacking. Using the Bycatch Risk Assess-
ment (ByRA) toolkit, based on InVEST open-source models, we chose 4 field sites in Southeast
Asia with varying amounts of data on marine mammals and fishing occurrence: Trat province in
the eastern Gulf of Thailand, the Sibu-Tinggi Islands and Kuching Bay, Malaysia, and Kien Giang
Biosphere Reserve in southwestern Vietnam. These field sites have similar species of coastal mar-
ine mammals, small-scale and commercial fisheries, and support for research from universities
and/or management. In Thailand and Kuching, results showed changing patterns of fishing and
Irrawaddy dolphin Orcaella brevirostris habitat use across seasons, showing how bycatch risk
could change throughout the year. Risk maps for dugongs Dugong dugon in peninsular Malaysia
highlighted patterns of bycatch risk concentrated around a mainland fishing pier, and revealed
high risk in a northern subregion. In Vietnam, first maps of bycatch risk for the Irrawaddy dolphin
showed the highest risk driven by intensive use of gillnets and trawling gear. ByRA pinpointed
areas of spatial and seasonal bycatch exposure, and estimated the consequence of bycatch on
local species, providing managers with critical information on where to focus bycatch mitigation
and meet new global standards for US Marine Mammal Protection Act and other international
regulation (e.g. Official Journal of the European Union 2019; Regulation 2019/1241) compliance.
The toolbox, a transferable open-source tool, can be used to guide fisheries management, marine
mammal conservation, spatial planning, and further research.
KEY WORDS: Incidental bycatch · Marine mammals · Spatial risk assessment · Open-source GIS
toolkit · Small-scale fisheries · Southeast Asia
Contribution to the Special ‘Marine vertebrate bycatch: problems and solutions’
Getting to the bottom of bycatch: a GIS-based toolbox
to assess the risk of marine mammal bycatch
Ellen Hines1,*, Louisa S. Ponnampalam2, Chalatip Junchompoo3, Cindy Peter4,
Long Vu5, Thien Huynh6,7, Marjolaine Caillat8, Andrew F. Johnson9,10, Gianna Minton11,
Rebecca L. Lewison12, Gregory M. Verutes13,14
1Estuary & Ocean Science Center, and Department of Geography & Environment, San Francisco State University, Tiburon,
CA 94920, USA
2The MareCet Research Organization, 47630 Subang Jaya, Selangor, Malaysia
3Department of Marine and Coastal Resources, Ministry of Natural Resources and Envir onment, Chaeng WatthanaRoad,
Lak Si District, Bangkok 10210, Thailand
4Universiti Malaysia Sarawak, Jalan Datuk Mohammad Musa, 94300 Kota Samarahan, Sarawak, Malaysia
5Vietnam Marine Megafauna Network, Center for Biodiversity Conservation and Endangered Species, 24, Street No 13,
Lakeview City, Ho Chi Minh City,Vietnam
6Southern Instituteof Ecology,Vietnam Academy of Science and Technology,01 Mac Dinh Chi, BenNghe, District 1, HoChi Minh City,
7Graduate School of Natural Scienceand Technology, Kanazawa University, Kakumamachi, Kanazawa, Ishikawa920-1164, Japan
8Environmental Defense Fund, San Francisco, CA 94105, USA
9MarFishEco Fisheries Consultants, 67/6 BrunswickStreet, EdinburghEH7 5HT, UK
10The Lyell Centre, Institute of Life and Earth Sciences, School of Energy, Geoscience, Infrastructure and Society,
Heriot-Watt University, Edinburgh, EH14 4AS, UK
11Megaptera Marine Conservation, 2242 PT Wassenaar, The Netherlands
12Department of Biology, San Diego State University, CA 92182, USA
13Faculty of Political and Social Sciences, Universidade de Santiago de Compostela, Praza do Obradoiro, 0,
15705 Santiago de Compostela, A Coruña, Spain
14Campus Do*Mar, International Campus of Excellence, 36310 Vigo, Spain
Endang Species Res 42: 37–57, 2020
Fisheries bycatch, the unintended capture of non-
target species, has been recognized as the most seri-
ous threat to marine mammals for decades (Reeves et
al. 2013). A rough estimate of marine mammal by -
catch suggests an estimated 300 000 cetaceans are
taken each year by fisheries globally (Read et al.
2006). In many countries, fisheries bycatch of marine
mammal species is poorly monitored or regulated, so
impacts of bycatch on local populations are not well
understood. Even in countries where marine mam-
mal abundance and distribution estimates are avail-
able, gaps in data on fishing effort and gear use,
bycatch rates, and the fate of animals post-capture
are key obstacles that impede the ability to charac-
terize or quantify the risk of fisheries bycatch on res-
ident marine mammal populations (Goldsworthy &
Page 2007, Soykan et al. 2008, Hines et al. 2012,
2015a, Teh et al. 2015). Due to a lack of resources that
impedes local scientific capacity, data limitations are
often greatest in developing countries, where marine
fisheries can comprise a significant contribution to
local and even national economies (Briscoe et al.
The 2017 International Affairs and Seafood Inspec-
tion rule from the NOAA Office of International
Affairs in the United States stipulates that seafood
imports to the United States need to comply with
Marine Mammal Protection Act (MMPA) regulations
that require the monitoring and reduction of marine
mammal bycatch (Federal Register 2016). Countries
with relevant fisheries exporting to the United States
have 5 yr from January 2017 to document their com-
pliance. In many of these countries there are signifi-
cant data gaps on marine mammal distribution and
abundance and bycatch rates, especially in develop-
ing countries (Williams et al. 2016, Johnson et al.
2017). The MMPA rule and similar regulations from
the European Union (e.g. European Union Council
Regulation 2019/1241; Official Journal of the Euro-
pean Union 2019) have intensified the need for better
data to monitor and report marine mammal bycatch
and fisheries-related population impacts.
Helping governments that rely on fisheries exports
to address these gaps has been one of the drivers for
the creation of the Bycatch Risk Assessment (ByRA)
toolbox. This toolbox allows the spatial/temporal
assessment of bycatch risk using any amount of data,
identifying areas for critical research and possibly
immediate management actions while accounting for
reliability and robustness in toolbox results. The
need for more data to inform conservation manage-
ment and policy can also delay conservation action.
There is an equally strong need to make better use of
existing data to develop bycatch risk assessments for
marine mammals of conservation concern, and use
these data to improve population-level estimates and
in form management strategies (Stelzenmüller et al.
2015). Risk assessments identify, analyze, and evalu-
ate the likelihood or probability of an event happen-
ing, and the consequences of that event (Gibbs &
Browman 2015).
Spatial risk assessments in a geographic informa-
tion systems (GIS) environment are valuable tools
that incorporate diverse data quality to evaluate
areas where management can be most effective
(Grech et al. 2008). While high-precision and high-
resolution data yield assessments with low levels of
uncertainty (as long as uncertainty is accurately
described and accounted for), even low-resolution
information can be used to characterize bycatch risk
and prioritize sites, gear types, and seasons for mon-
itoring, and to guide future data collection efforts
(Hoffman & Hammonds 1994, Briscoe et al. 2014).
Reducing bycatch to sustainable levels for marine
mammal populations will require collaborative ef -
forts among scientists, conservation organizations,
re source managers, industry, and local communities.
This integration must include an economic perspec-
tive and account for the behavior and decision-mak-
ing of fishers (Lewison et al. 2004, Teh et al. 2015).
The nature of small-scale fisheries frequently makes
traditional means for monitoring and quantifying by -
catch nearly impossible: vessels are often too small to
host on-board fisheries observers; fishing areas are
too remote and dispersed to allow effective monitor-
ing of landing sites; and resources are often lacking
to implement remote electronic monitoring or other
means to document bycatch. At the same time, a high
proportion of these fisheries are using gillnets in
nearshore areas, a combination known to be the
main driver for decreases in endangered populations
of small cetaceans around the globe (Brownell et al.
2019). As such, in order to plan effective mitigation
measures, even in the absence of direct observations
of bycatch, tools are desperately needed to allow the
estimation of bycatch risk in areas where fisheries
and coastal marine mammals overlap. Bycatch situa-
tions and therefore solutions are local and place-
based, controlled by diverse biophysical, cultural,
economic, and political criteria. However, common-
alities in many of these issues allow the application of
this framework to guide the creation and analysis of
a spatially explicit bycatch risk analysis locally and
Hines et al.: GIS-based bycatch risk assessment
The idea for ByRA came from several approaches
that spatially modeled the impact of human activities
on the abundance and distribution of different taxa.
Samhouri & Levin (2012) and Arkema et al. (2014)
estimated risk scenarios using the dimensions of con-
sequence and exposure and created an open-source
habitat risk assessment (HRA) model (Sharp et al.
2018) that was incorporated into the InVEST toolbox
for the Natural Capital Project (https://naturalcapi- Specifically for marine
mammals, Briscoe et al. (2014) modeled the risk of
fisheries bycatch for an area of limited data for
dugongs Dugong dugon along the coast of Sabah,
Malaysia. The resulting risk surface map incorpo-
rated existing data and identified areas of high risk
for areas where dugong surveys had and had not
been conducted.
Modeled after the HRA and the Briscoe et al. (2014)
methods, our toolkit visually presents spatial and
temporal bycatch risk information, making it easier
for scientists and managers to identify and predict
areas of likely persistent bycatch and test manage-
ment scenarios to reduce bycatch (Lewison et al.
2009, Briscoe et al. 2014).
Here, we tested the newly developed ByRA toolbox
to create a spatially explicit geodatabase for 4 fishing
sites across 3 countries. This process used existing
data to characterize the distribution and abundance
of marine mammal species of conservation concern,
fisheries occurrence and interaction rates, and other
anthropogenic factors that characterize bycatch risk.
These efforts pointed to the need for a transferable
open-source tool that supports spatial decision-
making/planning to reduce and prevent marine
mammal bycatch, and provided a means for develop-
ing countries to comply with recent international reg-
ulations on fisheries exports to the US market.
2.1. Study sites
Dunn et al. (2010) and Stewart et al. (2010) under-
took a comprehensive, multi-year study to quantify
the spatial extent of fishing effort and density in sev-
eral coastal regions of the world’s oceans. One of
these regions, Southeast Asia, is a region of high spe-
cies biodiversity coupled with high fishing density
(Roberts et al. 2002, Stewart et al. 2010). The Interna-
tional Union for Conservation of Nature (IUCN) Spe-
cies Survival Commission’s Cetacean Specialist
Group’s 2002−2010 Conservation Action Plan recom-
mends research initiatives to identify special areas of
‘cetacean abundance for special conservation atten-
tion’ in Southeast Asia, as well as to document ceta-
cean bycatch in these areas (Reeves et al. 2003, p. 60).
We chose our study sites Trat province on the
eastern Gulf coast of Thailand, Sibu-Tinggi Islands
and Kuching Bay, Malaysia, and the Kien Giang Bio-
sphere Reserve in southwestern Vietnam —based on
established collaborations with local scientists and
varying amounts of pre-existing data with which to
test our toolbox model (Fig. 1). In all 3 countries, we
had the support of local management agencies and
researchers. Furthermore, Thailand and Vietnam are
also the largest Southeast Asian exporters of com-
mercial fish to the United States (with a value of
USD 156 261 104 and 116 776 983 in fish products in
2018, respectively) (NOAA Office of Science and
Techno logy Commercial Fisheries Statistics). The
study sites are described in the order of overall data
availability, from Thailand, which has the most
research on animals and fisheries, to Vietnam, where
little research has been done.
2.1.1. Trat Province, Thailand
Trat Province is located along the eastern coast of
the Gulf of Thailand and is an important fishing area
for local communities (Fig. 1). Trat is also habitat for
Irrawaddy dolphins Orcaella brevirostris, Indo-Pacific
finless porpoises Neophocaena phocaenoides, and
Indo-Pacific humpback dolphins Sousa chinensis.
Studies of local cetacean populations have largely
been conducted by boat and aerial surveys, and have
demonstrated that the most abundant local cetacean
species is the Irrawaddy dolphin (Hines et al. 2015b).
Aerial and ship-based line transect and photo-identi-
fication surveys have been conducted since 2003, and
are ongoing (e.g. Junchompoo et al. 2013). Five years
of line transect surveys yielded an average relative
abundance estimate of 423 (95% CI = 252−734) Irra -
waddy dolphins (Hines et al. 2015b).
The most common fishing gears used by the local
small-scale fishery communities in Trat Bay are gill-
nets and crab traps (54% and 25%, respectively),
although a total of 14 types of fishing gear are used in
the area. The high density of fishing gear is likely the
major threat to dolphins in this area (Junchompoo et
al. 2013).
The Department of Marine and Coastal Resources
is the main agency responsible for Thailand’s marine
resources. Research on marine mammals and their
conservation is conducted in collaboration with many
Endang Species Res 42: 37–57, 2020
additional agencies including the Department of
Fisheries, the Department of National Parks Wildlife
and Plant Conservation, the Royal Thai Navy, and
local NGOs.
2.1.2. Kuching Bay, Sarawak, Malaysia
Kuching Bay is located approximately 50 km from
Kuching, Sarawak, in East Malaysia, and includes
Salak-Santubong Bay, Bako-Buntal Bay, and several
interconnecting rivers (Fig. 1). The 2 major estuaries/
bays are shallow, with depths not exceeding 10 m as
far as 15 km offshore. The study site within Kuching
Bay includes several terrestrial and marine protected
areas, protected under Sarawak’s National Parks and
Nature Reserves Ordinance (Chapter 27, 1998, http:// Ordinance / ORD_
CAP. % 2027%20watermark.pdf). All species of ceta -
ceans are protected under the Wild Life Protection
Ordinance (Chapter 26, 1998, https://www. sarawak laws/ wildlife _ protection_ ordinance
98_chap26.pdf) and anyone who kills, hunts, sells, or
captures cetaceans may be subjected to fines and/or
imprisonment. The agency responsible for enforcing
this ordinance is the Sarawak Forestry Corporation.
In 2008, the Sarawak Dolphin Project was initiated
and housed under Universiti Malaysia Sarawak
(UNIMAS) with the aim of collecting baseline data on
the distribution and habitat use of coastal dolphins in
Sarawak. Over subsequent years, the UNIMAS team
has conducted monthly boat-based surveys of Irra -
waddy dolphins, finless porpoises, and Indo-Pacific
humpback dolphins, as well as observations of their
interactions with local fisheries. Photo identification
studies by Minton et al. (2011, 2013) yielded esti-
mates of 233 Irrawaddy dolphins (95% CI = 151−
360), while line transect analyses resulted in an esti-
mate of 149 Irrawaddy dolphins (95% CI = 87−255) in
the bay (Minton et al. 2013).
Approximately 1150 fishers operate in the Kuching
Bay area, for both local consumption and commercial
sale (Annual Fisheries Statistics 2018). Fisheries ac-
tivities were recorded during boat-based surveys be-
tween March 2011 and August 2013, and were com-
plemented by interviews with fishers from 5 coastal
villages surrounding Kuching Bay. Both interviews
and direct observations revealed that gillnets are the
most commonly used gear, with a clear post-monsoon
(March−May) seasonal peak in the presence of at-
tended gillnets, when fishers stayed at a net, and a
peak in unattended gillnets, when fishers left a net
Tra t
Kien Giang
0 250
Biosphere Reserve
500 Km
Trat Province
Kuching Bay
Kien Giang
Biosphere Reserve
Sibu-Tinggi Islands
Tra t Ba y
Ban Mai Rut
Khlong Yai
Besar I sland
Sibu Island
Tinggi Island
Tan j un g
Bako-Bun tal
Salak Telaga
Air Rivers
Ca Mau
Ha Tien
Nam Du
Son Hai
Ba Lua
Fig. 1. Locations of the 4 study sites: (A) Trat Province, Thailand, (B) Kuching Bay, Sarawak, East Malaysia, (C) Sibu-Tinggi
Islands, Johor Province, Malaysia, and (D) the Kien Giang Biosphere Reserve, Vietnam
Hines et al.: GIS-based bycatch risk assessment
between setting it and pulling it in, in the pre-mon-
soon season from September to December. Peter et
al. (2016) recorded the relative density of ob served
fishing activity, which indicated a strong overlap be-
tween the primary fishing areas and the preferred
habitats of Irrawaddy dolphins, which are concen-
trated in rivers, river mouths, and close to the shore.
2.1.3. Sibu-Tinggi Islands, Johor, Malaysia
The Seribuat Archipelago is a group of tropical
islands located in the South China Sea, 4−6 nautical
miles, n miles) off the east coast of Johor state in Pen -
insular Malaysia (Fig. 1). These islands, particularly
the Sibu group and Tinggi (SBTG), are core habitats
for a small population of dugongs Dugong dugon, the
last remaining area in Peninsular Malaysia where the
species is reliably found (Ponnampalam et al. 2015).
The seagrass meadows, especially at Besar, Sibu, and
Tinggi, have been identified as important feeding
grounds for dugongs as well as sea turtles (Ponnam-
palam et al. 2015).
The islands in the Seribuat Archipelago have been
Federal Marine Parks since 1994, and are managed
by the Division of Marine Parks and Resource Man-
agement of the Department of Fisheries Malaysia, a
federal government agency. Each island’s marine
park boundary stretches 2 n miles from the lowest
water mark and constitutes a complete no-take zone;
collectively, the island marine parks are branded as
the Sultan Iskandar Marine Park. In 2016, the Johor
state government proposed and declared its commit-
ment for a Johor Dugong Sanctuary to be estab-
lished, the status of which, as of this writing, is still
pending official gazette from the federal govern-
ment. Dugongs are considered an endangered spe-
cies in Malaysia, and are protected in Peninsular
Malaysia under the Fisheries Act 1985. However,
seagrass meadows have no specific legal protection
unless they fall within marine park waters, an incon-
sistency that may prove important to the future of
Malaysia’s dugong populations.
Research since 2010 has revealed that Sibu Island
is the main feeding and nursery ground of dugongs
in the wider area, while Tinggi Island is a feeding
ground and vocal hotspot for dugong communica-
tions (Ponnampalam et al. 2015). Results of aerial sur-
veys estimated that the population is small, with
daily maximum counts not exceeding 20 animals
sighted. Nearly 24% of sightings were of mother−calf
pairs, indicating a reproducing population (L. S. Pon-
nampalam unpubl. data).
The human populations of the islands are com-
posed of local villagers, resort workers, and tour
operators who run snorkeling, SCUBA diving, and
angling trips. While the waters out to 2 n miles from
the islands are a no-take zone, there are still local vil-
lagers who work as artisanal fishers and fish for sub-
sistence close to shore. The main types of fishing
gear used by artisanal fishers in this site are 3-lay-
ered monofilament trammel nets, driftnets, rods, and
cage traps locally known as bubu. Fisheries regula-
tions in Malaysia state that commercial trawlers are
to operate at least 5 n miles from shore. However,
aerial surveys in 2010 and 2014−2016 yielded obser-
vations of encroachment in waters less than 5 n miles
from the coast and within the no-take zones of the
marine park (L. S. Ponnampalam unpubl. data). In
total, there were 1607 local and 3129 foreign fishers
registered in the local area in 2018 (Department of
Fisheries, Malaysia 2018).
Since 2015, there have been at least 18 recorded
cases of dugong deaths from around Sibu and Tinggi
Islands, all of which were juveniles. Some had evi-
dence of death resulting from interactions with hu-
man activities. At least 2 of those dugongs had died
from being caught in an illegal longline de ployed in
the area and known locally as rawai hantu (ghost
longline). One longline alone is fitted with 10 000
fishing hooks (L. S. Ponnampalam unpubl. data).
2.1.4. Kien Giang Biosphere Reserve
The Southwest Gulf of Vietnam is in the eastern
Gulf of Thailand. Extending from the Cambodia bor-
der to Ca Mau Cape, the total coastline is 312 km
(Fig. 1). The UNESCO Kien Giang Biosphere Re serve
(KGBR) was designated in 2006. Phú Quc Island is
the largest of 105 islands in the reserve. As a
transition zone between the Gulf of Thailand and
South China Sea, this area is recognized by the Viet-
namese government as an important fishing zone. Re-
ports on provincial fisheries can be found in the Kien
Giang Department of Fisheries, which only ac counts
for registered boats. According to those reports, in
2014, there were 10 880 registered fishing boats in
Kien Giang province and total catch was 636 170 tons,
or 20% of the total seafood landings in Vietnam.
To date, research on marine mammals in Vietnam,
including the KGBR, has not been conducted regu-
larly, nor with enough coverage spatially to assess
which species are present or the status of any popu-
lation. Vu (2014) conducted boat surveys for ceta -
ceans in 2014−2015 along the northeast coast of the
Endang Species Res 42: 37–57, 2020
Reserve and around Phú Quc Island to the west
(Fig. 1), resulting in 4 sightings of 19 Irrawaddy dol-
phins, and 2 of finless porpoises. The non-profit Viet-
nam Marine Megafauna Network has been actively
involved in marine mammal conservation in KGBR
since 2014 (Vu 2014). The other local NGO, Wildlife
at Risk, has regularly conducted educational pro-
grams concerning dugong conservation around Phú
Quc Island in western KGBR since 2010.
When bycatch takes place inside the boundary of
KGBR (Fig. 1), it immediately falls under the scope of
the KGBR Management Board. KGBR’s marine mam-
mals, which are categorized as aquatic resources by
Vietnamese law, are also under the management of
the Kien Giang Department of Natural Resources
and Environment. Marine mammal bycatch, directly
related to fisheries, is the responsibility of the Fishery
Agency of the Kien Giang Department of Agriculture
and Rural Development.
2.2. ByRA overview
ByRA assesses the risk of bycatch based on the spa-
tial and temporal coincidence of ranked probabilities
of overlap between a species occurrence and fishing.
Based on the Habitat Risk Assessment (HRA) tool
created for the Natural Capital Project InVEST tool-
box (Sharp et al. 2018), risk to species caused by a
stressor is calculated as the weighted average of ex -
posure, or the degree to which a species experiences
stress due to a human activity (spatial/ temporal over-
lap, intensity, status of management strategies), and
consequence, the species-specific re silience and sen-
sitivity to a stressor (mortality, life stages affected,
etc.) (Samhouri & Levin 2012, Arkema et al. 2014).
Ex posure is the overlap between a species’ distribu-
tion and the extent of a human activity in space and
time. Consequence, in terms of sensitivity, is an
expert assessment of how a population will respond
to an impact. In terms of resilience, consequence is
based on a scientific assessment of the population
dynamics and life history of a species (Arkema et al.
2014). The collaborators for each site populated ex -
posure and consequence tables with ratings that
scored each stressor (see Table S1 in the Supplement
at www. int-res. com/ articles/ suppl/ n042 p037 _ supp.
pdf). Bycatch risk was then calculated as the straight
line (Euclidean) distance of the summed exposure
and consequence scores (Sharp et al. 2018). If a stres-
sor was not applicable in a specific area, a score of
zero (0) omitted that stressor from further assess-
ment. The ByRA output was a series of GIS layers,
showing these risk scores for each site or region, and
a map layer for the species classified by the relative
amount of risk (high/medium/low).
Beyond the HRA, we developed 3 new spatially ex -
plicit criteria specifically for the ByRA tool (Table 1).
The first, which assessed the current status of man-
agement actions, was based on marine and fisheries
management information to indicate where manage-
ment strategies have been identified or implemented.
Intensity was based on fishing gear-type density, and
was computed by the model. The intensity of fishing
activity by gear type often varied across space and
could be mapped as a surface using spatial interpola-
tion tools available in a GIS. Likelihood of interaction
between fisheries and species was also calculated by
the toolbox as the sum of habitat suitability (see be-
low) ranking (1−3) and a combined fishing occur -
rence and gear-type intensity rating (1−3), then re-
classified from lowest to highest (1−3). The rationale
was that if both a species and gear had a high proba-
bility of being present in a given area, the likelihood
of interaction or bycatch was also high.
2.3. Data compilation
We collected data on the physical and biological en-
vironment, as well as animal sightings from surveys
and interviews from each field site. ByRA was specifi-
cally formatted to include the most commonly used
data formats. We also reviewed the literature to
access the most relevant, recent local data. Table 2
shows a preliminary assessment of existing local data
Risk level 3 2 1 No score (0)
Current status of Management strategy Management strategy No strategy Not known
management actions identified and implemented identified identified
Intensity High intensity Medium intensity Low intensity Not known
Likelihood of interaction High likelihood Medium Low Not known
Table 1. Specialized exposure criteria for the Bycatch Risk Assessment (ByRA) toolkit
Hines et al.: GIS-based bycatch risk assessment
readily available. Data on fishing effort by season,
fishing areas, and the type of gear used were ob tained
from surveys, interviews or expert knowledge and in-
corporated into layers of fisheries risk. While informa-
tion from the scientific literature is the most useful for
objectivity and repeatability (Hobday et al. 2011,
Arkema et al. 2014), expert opinion (Teck et al. 2010,
Maxwell et al. 2013) and interview responses (Hines
et al. 2005, Ortega-Argueta et al. 2012, Pilcher et al.
2017), even with potential biases and misinformation,
are especially useful in data-poor situations. There is
a precedent for interviews especially to be an effi ci -
ent, low-cost method for gathering data to contribute
to knowledge on relative abundance and fisheries
risk that contribute to conservation and management
planning (Hines et al. 2005, Pilcher et al. 2017).
For each site, we chose to focus on the more abun-
dant species seen in surveys. For example, in Trat
Province, Thailand, 7 yr of line transect boat survey
data were available from 2008 to 2014 (dry season),
and 2014 to 2016 (wet and dry seasons), with the Irra-
waddy dolphin being the most numerous cetacean
species observed (Junchompoo et al. 2013, Hines et
al. 2015b, Jackson-Ricketts et al. 2020). In the Sibu-
Tinggi Island area, the distribution and habitat use of
the local population of dugongs had been studied
through aerial surveys during the dry season in 2010
(Ponnampalam et al. 2015) and from 2014 to 2016,
along with research on foraging in extensive seagrass
beds around the islands and the distribution of human
activities (L. S. Ponnampalam unpubl. data). In Kuch-
ing Bay, Sarawak, both line transect and photo-identi-
fication methods were used to analyse data gathered
from sightings in boat surveys from 2008 to 2013.
These surveys were conducted regularly throughout
the year, enabling us to analyze habitat use during the
post-monsoon (March−May), dry (May− September),
and pre-monsoon (September− December) seasons.
Irrawaddy dolphins were the most frequently encoun-
tered cetaceans at this site (Minton et al. 2011, 2013,
Peter et al. 2016). For the KGBR, during the Vu (2014)
boat surveys, Irrawaddy dolphins were most com-
monly observed, but the numbers of all sightings
were too low for abundance estimation. We listed the
Table 2. Preliminary assessment of existing data in each field site
Trat Province, Thailand Kuching, Malaysia Sibu-Tinggi Islands,
Kien Giang, Vietnam
ByRA species Irrawaddy dolphin
Orcaella brevirostris
Irrawaddy dolphin
Orcaella brevirostris
Dugong Dugong
Irrawaddy dolphin
Orcaella brevirostris
Environmental data Depth, distance to river
mouths, distance to
land/Also salinity, pH,
turbidity, chlorophyll a
& temperature 2008,
2009, 2012−2014
Depth, distance to river
mouths, distance to
land/also temperature,
pH, turbidity, tide level
& salinity 2009−2012
Depth, distance to
river mouths, distance
to land
Depth, distance to
river mouths,
distance to land/also
(2014−2015) and
salinity (2015)
Animal survey
transects and/or
Boat surveys 2008, 2009,
Boat surveys 2008−2013 Aerial surveys 2010,
2014−2016; boat
surveys 2014-2016
Boat surveys
Sightings 1674 882 1360 4
Fishing occurrences Boat surveys 2008, 2009,
Boat surveys 2011−2013 Aerial surveys 2010,
2014−2016; boat
surveys 2014−2016
Boat surveys
Fishing areas Expert knowledge Expert knowledge Community interviews
2010, 2015−2017
Community inter-
views and expert
knowledge 2014,
Interviews &
2008, 2009, 2012− 2015 2010−2012 2010, 2015−2017 2014, 2016
Protected areas Proposed National Parks/
Proposed Biosphere Reserve
Bycatch information Thai Department of
Marine and Coastal
Resource maps and
records/local stranding
No systematic reporting No systematic
No systematic
Endang Species Res 42: 37–57, 2020
types of fishing gear that are primarily seen in bycatch
incidents. These were divided into 5 common cate-
gories: nets (including purse seine and gillnets),
trawlers, pots and traps, longlines, and hook and line.
Specific gears in each of these categories are given for
each site in Table 3. We determined the risk levels of
each gear for each site for the consequence and expo-
sure tables (see Table S1 in the Supplement). Nets
were classified as representing the highest risk for by-
catch, followed by trawlers, longlines, pots and traps,
and, lastly, hook and line gear.
We gathered data on fishing occurrences, areas,
gear use, and seasonality of fishing for each site
(Table 2). Fishing occurrences, in this context, were
defined for each site by the type and amount of data
we were able to access. For most sites, fishing occur-
rences consisted of boat-based sightings, or aerial
sur veys, combined with interviews and/or expert
knowledge of areas where boats usually go and dur-
ing what season.
Bathymetric data for our field sites varied in quality
and quantity. The ByRA tool is able to incorporate
bathymetric depth of any format and resolution. We
created maps of bathymetry in the Sibu-Tinggi
Islands by georeferencing and digitizing a British
Admiralty Chart, and incorporated nearshore depth
points collected during ship surveys. For KGBR, we
digitized bathymetric charts from 1963 analog charts
from the US Naval Oceanographic Office. For Kuch-
ing and Trat, there were no bathymetric charts avail-
able with sufficient resolution. However, a depth
sounder was used to collect data during both ceta -
cean surveys. In GIS, we interpolated the depth data
into bathymetric surfaces using an inverse distance
weighted interpolation algorithm, which estimated
values over a surface by averaging the values of the
known points within a specified neighborhood of
each sampled point (Philip & Watson 1982).
We created between 2 and 8 subregions within
each area based on conservation, geopolitical, and
ecological factors (Fig. 2). For Trat, we used the 2-
region study areas defined by the Thai Department
of Marine and Coastal Resources for surveys in the
wet southwest monsoon season, between April and
November, and the 3-region strata used for the dry
season (November−March) boat-based surveys (2008
to 2016) (Hines et al. 2015b). We divided Kuching
Bay and its expansive river system into the 4 strata
described in Minton et al. (2013) and Peter et al.
(2016). In the Sibu-Tinggi islands, our boundary con-
tained potential management zones based on
dugong distribution data in areas where the MareCet
Research Organization had conducted their aerial
monitoring surveys. We created 4 subregions based
on management strategies (e.g. no-take, no trawling)
being considered for the critical dugong habitats. In
KGBR, we included areas of less than 20 km from the
coast bounded by the management boundaries. We
then divided this area into the 7 survey strata used by
the Vietnam Marine Megafaunal Network (Vu 2014).
We created an eighth zone within the northern
extent of the KGBR to represent the area, partly in
Vietnam, monitored by Marine Conservation Cam-
bodia, an NGO based in nearby Cambodia.
Nets Trawlers Pots & traps Longlines Hook & line
Trat, Thailand Gillnet (crab, shrimp) Trawlers Crab trap Longline − hook Single hook
Purse seine Push net Octopus trap
Kuching Bay, Gillnet Trawlers Pots & traps Longline − high Rod line
Malaysia Set net (nylon) Longline − low
Drift net
Trammel net
Sibu-Tinggi Islands, Gillnet Trawlers Traps Longline − bottom Hook & line
Malaysia Purse seine
Drift net
Kien Giang, Anchovy purse seine Single trawl Crab trap Fish hooks & Squid hook
Vietnam Mackerel purse seine Pair trawl Cuttlefish trap lines and line
Purse seine with lights Electric trawl Octopus trap
Bottom gillnet
Surface gillnet (sardine)
Crab trammel net
Small mesh trammel net
Frame-attached set net
Table 3. Common groupings of specific gears commonly seen in marine mammal bycatch for each field site
Hines et al.: GIS-based bycatch risk assessment
2.4. Habitat modeling
An important input layer for the ByRA incorporated
the best available information on species distribu-
tion, from which we created habitat models for each
species at each location. Habitat models are impor-
tant tools for linking cetacean observations to ecolog-
ical variables and identifying critical habitat (Redfern
et al. 2006, Gregr et al. 2013). The models used to
delineate habitat depended on the number of sur-
veys and/or animals sighted. Depth, distance to land,
slope, and distance to river mouths have been shown
by numerous researchers to be commonly important
measures of habitat suitability for dugongs and
coastal cetaceans, including Irrawaddy dolphins (see
Minton et al. 2011, Briscoe et al. 2014 [for dugongs],
Peter et al. 2016, Jackson-Ricketts et al. 2020). When
enough survey-based sightings were available, such
as in Trat, Kuching Bay, and the Sibu-Tinggi Islands,
we used the modeling software Maxent, which is
suitable for small sample sizes (Pearson et al. 2007,
Elith et al. 2011, Briscoe et al. 2014). Maxent is a
presence-only data model that quantifies the statisti-
cal relationship between predictor environmental
covariates at locations where a species has been
observed, versus background locations in which no
species have been observed within the studied
region (Phillips et al. 2006). We used the R package
ENMeval (Muscarella et al. 2014) to apply Maxent
across a range of different locations in setting and
balancing model fit and predictive ability (Mus-
carella et al. 2014, Rhoden et al. 2017). As the ByRA
output was a representation of risk scores classified
to 3 levels (high/medium/low), the Maxent suitable
habitat outputs were also divided into 3 categories
based on the probability of occurrence predicted by
the best model for each cell of the map. Please see
the ‘Habitat modeling’ section in the Supplement for
more information.
In Vietnam, we had only 4 sightings of Irrawaddy
dolphins. For sample sizes below 10, Maxent results
are less robust (Pearson et al. 2007). Briscoe et al.
(2014) first showed how a rule-based GIS approach
could be used to designate areas of marine mammal
habitat in an area with little data, in that case,
dugongs along northern Sabah, Malaysia. We incor-
porated and improved on that method for the ByRA
in the KGBR. Based on the environmental variables
Muang Trat
Muang Trat
Muang Trat
Khlong Yai
Khlong Yai
Khlong Yai
Zone 3
Zone 3
Zone 3
Zone 4
Zone 4
Zone 4
Zone 2
Zone 2
Zone 2
Zone 1
Zone 1
Zone 1
Fig. 2. Subregions for the 4 Southeast Asian field sites. (A) Three subregions for Trat in the dry season. (B) Northern and southern
subregions for Trat in the wet season. (C) Four zones in the Sibu-Tinggi Island area. (D) Kuching Bay subregions: 1, Santubong-
Salak Bay; 2, Bako Buntal Bay; 3, Salak Telaga Air Rivers; and 4, Santubong Buntal River. (E) Zones 1-7 in the Kien Giang
Biosphere Reserve, Vietnam. Zone 8 is along the Vietnam/Cambodia border
Endang Species Res 42: 37–57, 2020
found relevant for Irrawaddy dolphins (see above),
we used GIS to create spatial overlay maps of bathy -
metry, distance to land, and river mouths to depict
levels of habitat use.
2.5. Uncertainty standards
Based on the data available, as well as the spatial
and temporal extent, we used a stoplight approach to
catagorize input and output uncertainty for each site.
We first created a reference table of uncertainty stan-
dards from which we could judge the existing infor-
mation available (Table 4). Data characterized as
green, or those in which we had the most confidence,
were based on robust and defensible research
methodologies. For example, sightings data in this
category were collected during formal transect or
photo-identification surveys, and sufficient sightings
were accumulated for the statistical analysis of rela-
tive abundance. Habitat distribution and suitability
here could be quantified based on sightings, loca-
tions, and a systematic collection of environmental
variables. Data on fishing gear and spatial occur-
rence, for a green classification, were collected for-
mally as well, usually noted during abundance sur-
veys, and optimally observing variations by season.
For bycatch or stranding data, the highest standard
included robust and sufficient data for an estimation
of a local bycatch rate.
The yellow category contained data collected
opportunistically, without formal surveys, with mini-
mal or no environmental variables collected. Habitat
use here was estimated only by overlaid environ-
mental variables and based on sightings and criteria
for the species in the literature. Estimates of fishing
gear, occurrence, and bycatch here were based on
community interviews or the opinion of local scien-
tists, NGO, or agency personnel.
The red, or least certain, category included surveys
with insufficent data for abundance analysis, or only
opportunistic sightings by fishermen obtained by
interview. Habitat use was based soley on estimated
environmental variables from the literature. Fishing
occurrence was estimated by expert opinion and
interviews. There was little to no effort in this cate-
gory to record incidences or species from bycatch or
stranding incidences.
3.1. Fishing occurrence
The spatial distribution of fishing occurrence and
gear is shown in Fig. 3. The top map (Fig. 3A) shows
the distribution of gear in Trat, Thailand, based on
sightings during boat surveys for Irrawaddy dolphins
(Jackson-Ricketts 2016). In the wet season (Fig. 3,
left), longline gear was prevalent, and seen close to
shore and up into the shallow bay to the north. In the
southern area of Trat province, longlines were seen
offshore. In contrast, there was much more fishing
during the dry season, with more gillnets seen
throughout the region, and no longline gear noted.
In Kuching Bay, net distribution data were also
gathered during boat surveys. Gillnets were the most
commonly used gear in all 3 seasons. Gillnet occur-
Table 4. A reference table of uncertainty standards for Bycatch Risk Assessment (ByRA) criteria classification
Green Yellow Red
Animal sightings
Data collected during formal
transect or photo ID survey;
data can be used to estimate
relative abundance
Sightings/photo ID collected
during opportunistic surveys
Few sightings collected, no
abundance estimation
possible; sightings only
available from interviews
Habitat suitability Estimated from modeling,
formal collection of environ-
mental variables
Rule-based estimation,
minimal environmental
variables collected
Criteria from other regions
used to estimate animal
Fishing occurrence/
gear type densities
Surveyed fishing occurrence
per unit of distance or time
over seasons
Spatial and/or temporal
distribution of fishing based
on interviews or expert
Sparse or incomplete data,
no geospatial or precise
locali zation of fishing
Bycatch/stranding data Data available from inter-
views or observers on boat or
stranding data; estimation of
bycatch rate possible
Relative estimation of
bycatch from interviews or
stranding data
No estimation of bycatch or
strandings available
Hines et al.: GIS-based bycatch risk assessment 47
052.5 10 Km
Bako-Buntal Bay
Santubong-Buntal River
Salak Tela ga Air Riv ers
Santubong-Salak Bay Bako-Buntal Bay
Santubong-Buntal River
Salak Tela ga Air Riv ers
Santubong-Salak Bay
Santubong-Buntal River
Bako-Buntal Bay
Santubong-Salak Bay
Salak Tela ga Air
Hook & line
Pots & traps
Dry season
Pots & traps
Hook & line
Hook & line
Pots & traps
Pots & traps
Wet season
Hook & line
Pots & traps
Fig. 3. Fisheries gear and occurrence
for each field site. (A) Gear use in
Trat, Thailand, during the dry (left)
and wet (right) seasons. (B) Fishing
gear in Kuching Bay, Sarawak,
Malaysia, for the (left to right) post-
monsoon, dry, and pre-monsoon sea-
sons. (C) Map of fishing occurrence
for the Sibu-Tinggi Island area,
Malaysia. (D) An estimation of fishing
occurrence and gear use for the Kien
Giang Biosphere Reserve, Vietnam
Endang Species Res 42: 37–57, 2020
rence was most frequent throughout the rivers and in
Bako-Buntal Bay in the post-monsoon and dry sea-
sons. In the pre-monsoon season, there was less gill-
netting in the bay, though activity was still high in
the rivers.
In the Sibu-Tinggi Islands, based on sightings of
boats during aerial surveys for dugongs, gillnets
were seen predominantly close to the mainland
shore, especially in the southern part of the region.
Hook and line gear were further out towards the
islands, and trawlers further offshore.
Estimated occurrence and gear distribution in the
KGBR were based on the expert opinions of local sci-
entists and Biosphere Reserve managers. We delin-
eated polygons for each gear with their guidance.
Gillnets and trawlers were most numerous, with
longlines and hook and line gear used around the
central and southern coasts of Phú Quc Island.
3.2. Habitat use
We used Maxent to model habitat use for Trat,
Kuching, and the Sibu-Tinggi Islands. The results of
the different models tested and an explanation of
models chosen can be found in the ‘Habitat model-
ing’ section of the Supplement. Here, we present the
distribution of the low, medium, and high habitat
suitability levels based on the contributed weights of
the different environmental variables per site and
per season.
3.2.1. Trat Province, Thailand
For both the dry and monsoon seasons, the variable
that contributed most to dolphin habitat was distance
to river mouth, with a percentage of 78.59% (SD =
1.47%) and 63.21% (SD = 2.42 %) for the dry and the
monsoon seasons, respectively. However, the corre-
sponding habitat differed between seasons (Table 5).
Fig. 4A shows the results of the habitat modeling for
Trat. During the dry season, the most suitable habitat
extended along the coast between Ban Mai Rut and
Khlong Yai. Around this highly suitable habitat, the
likelihood of dolphin presence decreased with an
increase of depth, the second most important envi-
ronmental variable (12.47%, SD = 1.51%). During
the monsoon, the most suitable habitat shifted away
from the coast and was divided into 2 main patches,
one offshore of the northern part of the study area
towards Trat Bay and one in the south from Khlong
Yai towards the Cambodian border. The distance to
river mouth continued to be the most influential
parameter but with a smaller contribution, while the
influence of depth became more important in com-
parison to the dry season (Table 5).
3.2.2. Kuching Bay, Sarawak, Malaysia
Fig. 4B shows the modeled probability of suitable
habitat conditions for the Irrawaddy dolphin data in
Kuching Bay organized by season. The ranges of the
different levels of habitat suitability, as well as the
contribution of the different environmental parame-
ters to each model, differed between seasons. During
the post-monsoon season, distance to land was the
environmental parameter contributing the most to the
model (47.42%, SD = 2.00%), whereas for the dry and
pre-monsoon seasons, the distance to the river mouth
(dry season, 55.26%, SD = 2.18%; pre- monsoon,
48.68%, SD = 2.94%) was the most important param-
eter (Table 6). Suitable habitat, independent of the
level of suitability (low, medium, or high), was further
offshore in the pre-monsoon season and close to the
land during the dry season. Highly suitable habitat
centered in the rivers and just outside the river
mouths during the dry season. During the post-
monsoon, the highly suitable habitat extended fur-
ther offshore along the coast into deeper waters.
3.2.3. Sibu-Tinggi Islands, Malaysia
In the Sibu-Tinggi Islands, the variables distance
to river mouth and distance to land contributed to
76% of the model prediction (Table 7). The most
suitable habitat for the dugongs was essentially
close to the islands, where there are known seagrass
meadows (Ooi et al. 2011, Ponnampalam et al. 2015)
Dry season Monsoon season
Environmental variable contributions (%)
Distance to river mouth 78.59 (1.47) 63.41 (2.42)
Distance to land 8.74 (2.36) 4.64 (1.40)
Depth 12.47 (1.51) 31.93 (2.43)
Slope 0.19 (0.29) Not used in model
Maxent validation statistics
Mean test AUC 85.05 (0.04) 85.85 (0.03)
Table 5. Summary of Maxent outputs for Trat, Thailand, with the
average rate of the contributions of each variable from the 10-fold
cross-validation, and standard deviation in parentheses (see
‘Habitat modeling’ in the Supplement). The variables with the
largest contributions for each season are in bold. AUC: area under
the curve
Hines et al.: GIS-based bycatch risk assessment 49
Low suitability
Medium suitability
High suitability
052.5 10 Km
Bako-Bunt al Bay
Santubong-Buntal River
Salak Telaga Air
Santubong-Salak Bay Bako-Bun tal Bay
Santubong-Buntal River
Salak Telaga Air Rive rs
Santubong-Salak Bay
Bako-Buntal Bay
Santubong-Salak Bay
Salak Telaga Air
Low suitability
Medium suitability
High suitability
Dry season
Low suitability
Medium suitability
High suitability
Wet season
Low suitability
Medium suitability
High suitability
Fish landings
Medium suitability
High suitability
Fish landings
Fig. 4. Habitat suitability model results
for each field site. (A) Irrawaddy dol-
phin habitat use in Trat, Thailand, dur-
ing the dry (left) and wet (right) sea-
sons. (B) Model results for Irrawaddy
dolphins in Kuching Bay, Sarawak,
Malaysia, for the (left to right) post-
monsoon, dry, and pre-monsoon sea-
sons. (C) Modeled habitat use for du -
gong in the Sibu-Tinggi Island area,
Malaysia. (D) Suitable habitat for Irra-
waddy dolphin in the Kien Giang Bio-
sphere Reserve, Vietnam
Endang Species Res 42: 37–57, 2020
(Fig. 4C). A large area of highly suitable habitat was
predicted between Sibu Island and the mainland
3.2.4. Kien Giang Biosphere Reserve
The most suitable habitat for Irrawaddy dolphins
was along the coast, from the west of Phú Quc
Island in Zone 7, through Zones 8 and 1, and in
Zone 2 into Rach Gia Bay. Medium ranked habitat
was along the eastern coast of Phú Quc, along Zone
1 and the coast of Zone 2. Medium ranked habitat
was also around the islands of Son and Nam Du
(Zones 3 and 4 respectively). As data were lacking
and uncertainty was high in this area, we decided not
to designate low quality habitat, so as not to assume
dolphins were not using these areas (a possible false
negative or Type II error).
3.3. Bycatch risk
Fig. 5 shows the spatial and seasonal risk of by -
catch. In the dry season in Trat province (Fig. 5A),
lower risk areas came right up to the
coast. The areas of highest risk were
concentrated offshore in the middle
strata and off the coast of Khlong Yai,
the major fishing port in the area, in the
bottom strata. In contrast, risk was less
concentrated in the wet season, further
offshore, but medium and high risk
areas were mapped in approximately
the same areas.
For Kuching Bay (Fig. 5B), Irrawaddy
dolphins and fishing activities shifted
substantially across 3 seasons (post-
monsoon, dry, and pre-monsoon) inside
the riverine system and coastal areas. The highest
by catch risk to dolphins in Kuching Bay during the
dry season was near the Salak Telaga Air and
Santubong-Buntal Rivers. During the pre-monsoon
season (September to December), predicted risk gra -
vi tated towards the estuary in southeast Santubong-
Salak Bay. Compared to the other seasons, bycatch
risk in the post-monsoon season was more dispersed
throughout the coastal marine areas and deeper
inside the rivers. More than half (57%) of the Santu -
bong River system included areas where bycatch risk
scores fell in the highest modeled suitable habitat
range for Irrawaddy dolphins during the dry season.
In the Sibu-Tinggi Islands (Fig. 5C), dugongs using
the marine areas in and around subregion 1 (Sultan
Iskandar Marine Park surrounding Sibu Island) were
at highest risk from gear used by vessels that likely
originated from the mainland pier at Tanjung Leman.
Intermediate to highest bycatch risk to dugongs cov-
ered almost two-thirds (62 %) of the entire subregion 1
area. Bycatch risk for dugongs was also high near
Mersing, a local fishing port. High to medium risk was
also concentrated around Besar Island to the north.
Bycatch risk in the KGBR (Fig. 5D) was highest on
the eastern side of Phú Quc Island (subregion 6),
around the islands of Son Hai in the Ba Lua archi -
pelago (subregion 1), and west of Hà Tiên city (sub-
region 5).
3.4. Uncertainty
Based on the standards shown in Table 4, we deter-
mined the level of uncertainty for each data category
for each site (Table 8). Fig. 5 also shows the stoplight
levels of uncertainty for the bycatch risk maps for
each area. All except KGBR show a combination of
yellow and green. KGBR, based on criteria in
Tables 4 and 5, has a combination of yellow and red.
Environmental variable contributions (%)
Distance to river mouth 40.09 (0.96)
Distance to land 34.96 (0.95)
Depth 24.78 (0.86)
Slope 0.16 (0.03)
Maxent validation statistics
Mean test AUC 88.48 (0.01)
Table 7. Summary of Maxent outputs for the Sibu-Tinggi Is-
lands, Malaysia, with the average rate of the contributions of
each variable from the 10-fold cross-validation, and stan-
dard deviation in parentheses (see ‘Habitat modeling’ in the
Supplement). The variable with the largest contribution is in
bold. AUC: area under the curve
Post-monsoon Dry Pre-monsoon
Environmental variable contributions (%)
Distance to river mouth 31.71 (1.88) 55.26 (2.18) 48.68 (2.94)
Distance to land 47.42 (2.00) 22.13 (1.93) 23.82 (2.11)
Depth 20.86 (0.98) 22.61 (0.68) 27.49 (1.51)
Maxent validation statistics
Mean test AUC 88.54 (0.03) 94.02 (0.01) 87.48 (0.04)
Table 6. Summary of Maxent outputs for Kuching Bay, Malaysia, with the
average rate of the contributions of each variable from the 10-fold cross-val-
idation, and standard deviation in parentheses (see ‘Habitat modeling’ in the
Supplement). The variables with the largest contributions for each season
are in bold. AUC: area under the curve
Hines et al.: GIS-based bycatch risk assessment 51
Low suitability
Medium suitability
High suitability
052.5 10 Km
Bako-Bunt al Bay
Santubong-Buntal River
Salak Telaga Air
Santubong-Salak Bay Bako-Bun tal Bay
Santubong-Buntal River
Salak Telaga Air Rive rs
Santubong-Salak Bay
Bako-Buntal Bay
Santubong-Salak Bay
Salak Telaga Air
Bycatch risk
Dry season
Bycatch risk
Wet season
Bycatch risk
Fish landings
Bycatch risk
Fish landings
Fig. 5. Bycatch risk assessment results
for each field site. (A) Bycatch risk for
Irrawaddy dolphins in Trat, Thailand,
duringthe dry (left)and wet (right)sea-
sons. (B) Bycatch risk for Irrawaddy
dolphins in Kuching Bay, Sarawak,
Malaysia, for the (left to right) post-
monsoon, dry, and pre-monsoon sea-
sons. (C) Bycatch risk for dugongs in
the Sibu-Tinggi Island area, Malaysia.
(D)Bycatchrisk for Irrawaddy dolphins
in the Kien Giang Biosphere Reserve,
Vietnam. See Table 4 for a description
of the stoplight levels of uncertainty
Endang Species Res 42: 37–57, 2020
Where formal surveys had been conducted, at Thai
and Malaysian sites, uncertainty was lowest, or
green. KGBR, with few formal surveys or sightings,
was rated the highest for uncertainty, or red. The
highest amount of environmental data for habitat
modeling was available for Trat. Both Malaysian sites
had some data collected, with more formal data in
Kuching. These sites were designated a combination
of green and yellow. In KGBR, only minimal environ-
mental data were collected. Both Trat and Kuching
Bay collected fisheries data during formal abundance
surveys. In Sibu-Tinggi, there were some data on
fisheries occurrence from interviews, sufficient
based on our standards to be labeled green and
yellow. There was some knowledge of fishing boat
locations and gear in KGBR from interviews with
local agency scientists. For all sites, data on bycaught
animals were available from interviews. In Thailand,
there was a stranding network and there were
agency scientists performing necropsies to deter-
mine cause of death. However, for no field site was
an estimation of by catch rate possible, so all sites
were marked yellow.
3.5. Exposure/consequence plots and gear
For Trat province during the dry season, the 3
regions had similar levels of exposure and conse-
quence, with the middle region showing more risk
(Fig. 6). Nets, including gillnets, purse seine, and sur-
rounding nets (Table 3), had the highest percentage
of risk for bycatch in all regions, followed by pots and
traps (crab traps and octopus traps) (Table S2). In the
monsoon season, there was a clearly higher risk of
consequence in the lower Khlong Yai region. Nets
were the riskiest gear in the Muang Trat region, but
were not present in Khlong Yai. Pots and traps were
the highest risk gear in this lower area.
In Kuching Bay, the exposure/consequence scores
of all 4 sites were spread along a range of exposure at
a high level of consequence (Fig. 6). In all 3 seasons,
the Santubong-Buntal River showed the highest level
of exposure and Salak-Santubong Bay the lowest. In
the dry season, Bako Buntal Bay was the highest in
consequence. In the Santubong-Buntal River, nets
showed the highest percentage of risk for bycatch in
all seasons, followed by pots and traps (Table S2 in
the Supplement). Nets in Kuching Bay included gill-
nets (hung from buoys at the surface), set nets (staked
to the seabed), drift nets (drifting on currents), and
trammel nets (2 to 3 layers of variable mesh size net-
ting) (Table 3). In Salak-Santubong Bay, the same
was true during the post-monsoon and dry seasons.
During the pre-monsoon, nets were followed in risk
by hook and line gear. In the Salak Telaga Air River,
nets and pots and traps were the riskiest; however, in
the pre-monsoon season, fishers only used nets and
hook and line gear. In Bako Buntal Bay, there were no
pots and traps. Nets had the highest risk of bycatch,
Table 8. Characterizations of uncertainty for bycatch toolbox criteria at each field site. See color scheme in Table 4
Trat Kuching Sibu-Tinggi Kien Giang
Animal sightings/ dis-
Systematic line transect
boat and photo-ID
Systematic line transect
boat and photo-ID
Systematic line
transect aerial survey
Systematic line transect
boat and photo-ID
survey but not
enough sightings to
abundance/ distribution
Habitat suitability Environmental data
collected with line
transect survey
Environmental data
collected with line
transect survey
Seagrass data and
mammal acoustics;
limited environmental
data collected during
Environmental data
partially collected
occurrence/gear type
and seasonality
Collected during line
transect survey and
data from interviews
Collected during line
transect survey and
data from interviews
Data from interviews From expert knowledge
and some interviews
Bycatch determination
coming from necropsies
of stranded animals;
regional stranding
of bycatch from
From interviews and
some records of
animals which had
stranded due to
fisheries interactions
of bycatch from
Hines et al.: GIS-based bycatch risk assessment
followed by trawlers, except in the
pre-monsoon season, when nets were
the only gear present.
The exposure/consequence plots for
the Sibu-Tinggi Islands showed
similar ratings with medium ex posure
and high consequence (Fig. 6). For all
4 zones, nets (gillnets and drift nets;
Table 3) were the riskiest gear, with
trawlers second (Table S2). In the
KGBR, all regions indicated high con-
sequence and a range of medium ex-
posure, with region 3 around Són Is-
land at the lowest level of exposure,
and region 8 in the northeast of the
Reserve along the Cambodian border
at the highest (Fig. 6). In all 8 zones,
nets were at the highest risk for by-
catch (see Table 3 for a list), with
trawlers second. In KGBR, the trawler
category included not only single
trawlers, but also paired and electric
The first test of the ByRA toolkit,
built on a diverse set of case studies,
put existing data from 3 SE Asian
countries into a cohesive risk assess-
ment and scenario framework that can
support marine mammal bycatch
management planning. The ByRA
results provided resident scientists
and managers with information on
areas and seasons of bycatch risk, as
well as the levels of risk for various
fishing gear at those times and loca-
tions, which could then support pre-
Sibu-Tinggi Islands Kien Giang Biosphere Reserve
Trat Dry Season
Trat Monsoon
3.0 2.0
3.01.5 2.5
3.0 2.0
3.01.5 2.5
3.0 2.0
3.01.5 2.5
3.0 2.0
3.01.5 2.5
3.0 2.0
3.01.5 2.5
3.0 2.0
3.01.5 2.5
3.0 2.0
3.01.5 2.5
Fig. 6. Exposure/consequence diagrams for
each field site. Panels labelled A–C repre-
sent (A) post-monsoon, (B) dry, and (C) pre-
monsoon in Kuching Bay. Regions with
marine mammals in a space with high expo-
sure to fishing activities and at a population
level that experiences a greater conse-
quence to bycatch are at the highest risk
levels on a scale from 1 to 3. See Fig. 2 for
subregions corresponding to numbers in
the Sibu-Tinggi Islands and the Kien Giang
Biosphere Reserve
Endang Species Res 42: 37–57, 2020
cautionary actions and policies, and inform carefully
designed research.
Irrawaddy dolphins are listed as Endangered on
the IUCN Redlist (Minton et al. 2017), and dugongs
are listed as Vulnerable (Marsh & Sobtzick 2015),
with fisheries bycatch recognized as the most signifi-
cant human-induced threat for both species (Reeves
et al. 2013, Brownell et al. 2019). As such, we should
use whatever data are available now to inform man-
agement and conservation interventions that can be
implemented as soon as possible, rather than waiting
for more data to be collected. Waiting can result in
continued population declines, as evidenced by the
vaquita and others (e.g. Turvey et al. 2007, Jaramillo-
Legorreta et al. 2019). As research efforts progress at
our sites, more substantiated scenarios can be run
and the uncertainty in toolbox results will decrease.
For each field site, the application of ByRA was dif-
ferent, and outputs led to different actual and pro-
posed management and research strategies.
In Thailand, the Department of Marine and Coastal
Resources presented the ByRA results to village
stakeholders to illustrate bycatch risk activities in
local areas. The mapped results were used in a mar-
ine protected area planning process (still ongoing) to
establish marine conservation zones in Trat Bay. The
Irrawaddy dolphin is considered a critical flagship
species in MPA planning. Bycatch risk maps showed
the different patterns of fishing and gear use, as well
as how habitat use changes for the Irrawaddy dol-
phins between the wet and dry seasons. Smaller
areas of risk in the wet season reflected less use of
fishing gear and fewer animals sighted (Junchompoo
et al. 2013). The ByRA results are being used to plan
fishing gear use restrictions and seasonal closures
based on how bycatch risk can change throughout
the year. Input on conservation zoning is being deter-
mined based on public hearings. The challenge of
implementing this planning process is how to bal-
ance local livelihoods with conservation-oriented
In Kuching Bay, the ByRA results, changing by sea-
son, especially in the rivers, aligned with what has
been observed in the field. Results showing high risk
areas will be used to engage local fishing communi-
ties and ask for their input on how to reduce the risk
of entanglement at various times of the year, for ex -
ample by ensuring that nets are not left unattended.
When combined with interview results, the mapped
results show that fishers from certain villages may be
more likely to be involved with entanglement inci-
dents than others. Interventions, such as awareness
raising workshops and disentanglement training, can
be focused on these villages. ByRA results will be
used to design robust trials for mitigation measures
in higher risk area e.g. the effectiveness of attend-
ing nets versus leaving them unattended, trials of
acoustic deterrent de vices on nets, and time/area clo-
sures. The risk maps will be used to engage local
government agencies and Sarawak Forestry, and
work on a collaborative strategy with them to trial,
and eventually introduce and enforce effective miti-
gation measures.
In the Sibu-Tinggi Island area, risk patterns were
seen to be driven by multiple factors including the
intensive use of gillnets, a gear known to entangle
du gongs (reflected in ByRA’s likelihood of capture
criterion), which are frequently observed in exten-
sive seagrass beds and shallow depths (highly suit-
able habitat). The ByRA output will be shared with
the Department of Fisheries Malaysia and the
Department of Marine Park Malaysia to assist them
with better management planning and enforcement.
These outputs will also be used as a basis for recom-
mending that the agencies use the M2 Marine Mon-
itor System to monitor vessel activities in real time,
so that illegal activities (such as ghost or unattended
longlines) may be intercepted. The M2 is a low-cost
radar system that detects and tracks vessel activity
in nearshore protected areas to support monitoring
and enforcement efforts (www. protectedseas. net).
The Marecet research group has been using the
ByRA output as guidance for further research plan-
ning, particularly which aspects of data collection
they need to improve. They are continuing to collect
better environmental data, and create more efficient
methods for monitoring bycatch and strandings.
For the KGBR, the management board of the re -
serve will use the risk maps to assist the local Fishery
Department and the People’s Committee of Kien
Giang Province in the management of the fishing
zones to reduce bycatch. Although there was a high
level of uncertainty regarding the area of high-
by catch risk in KGBR, the risk map allows the man-
agement board to raise concerns about bycatch in
KGBR to the higher level national authorities. Addi-
tionally, the scientists of the Vietnam Marine Mega -
fauna Network will utilize the results from the ByRA
toolkit to plan future research and monitoring efforts
in the highest risk areas. They will work to reduce
uncertainty and fill in data gaps, including methods
such as acoustics, formal line transects, and photo-
identification surveys to obtain more information on
marine mammal distribution, as well as badly needed
data on fisheries occurrence and gear use. The joint
efforts of the Vietnam Marine Megafauna Network
Hines et al.: GIS-based bycatch risk assessment
and the management board of the KGBR will ensure
the results of ByRA toolbox are translated into better
management and conservation planning.
By synthesizing and organizing bycatch risk as sess -
ment methods in an accessible framework, the reach
of our project extends beyond the local areas used to
demonstrate the toolkit. For further information about
the Southeast Asian project described in this paper,
see http://cons.scienceontheweb. net/ bycatch/.
At the time of this writing, workshops on how to
use the ByRA toolbox have been conducted by the
development team in Malaysia, Thailand, India, and
Vietnam. Workshops in other regions are scheduled
for the coming year. Our goal is for an open-source
toolbox to be available to download and use along
with a manual, now translated into Spanish.
This research represents the first regional view of
how these methods can support practitioners to esti-
mate marine mammal distribution, fisheries gear use,
and bycatch, and find effective measures to reduce
bycatch to sustainable levels. Syntheses of ByRA out-
puts can suggest management interventions by sub-
region, species, and gear type for targeting at-risk
areas and reducing risk. Spatially and temporally
explicit scenarios can be built and further improved
to evaluate conservation outcomes for additional taxa
with large-scale and transient habitat requirements.
Acknowledgements. Agencies and non-profit organizations
for the sites included the Department of Marine and Coastal
Resources (DMCR) in Thailand, the Kien Giang Biosphere
Reserve Management Board (KGBRMB), Department of
Marine Park Malaysia, the Institute of Biodiversity and Envi-
ronmental Conservation at University Malaysia Sarawak,
MareCet Malaysia, andSarawak Forestry. We especially ap -
preciated working with Marine Conservation Cambodia for
contributing data about Irrawaddy dolphins and fishing
effort along the northwestern boundary of Kien Giang Bio-
sphere Reserve. Thank you to the Bycatch Mitigation Initia-
tive of the International Whaling Commission for funding
the Spanish translation of our manual. We would like to
thank Nina Young for her support and inspiration, and Max
Czapanskiy for R advice. Funding for this project was from
the U.S. National Office of Atmospheric Administration
Office of International Affairs and Seafood Inspection Award
Number: NA16NMF4630338.
Annual Fisheries Statistics Malaysia (2018) https://www.iotc.
org/ sites/default/files/documents/2017/10/IOTC-2017-
WPDCS13-18_-_MYS.pdf (accessed 02 December 2019)
Arkema KK, Verutes GM, Bernhardt JR, Clarke C and oth-
ers (2014) Assessing habitat risk from human activities to
inform coastal and marine spatial planning: a demonstra-
tion in Belize. Environ Res Lett. 9(11): 114016
Briscoe DK, Hiatt S, Lewison R, Hines E (2014) Modeling
habitat and bycatch risk for dugongs in Sabah, Malaysia.
Endang Species Res 24: 237−247
Brownell RL Jr, Reeves RR, Read AJ, Smith BD and others
(2019) Bycatch in gillnet fisheries threatens Critically
Endangered small cetaceans and other aquatic mega -
fauna. Endang Species Res 40: 285−296
Department of Fisheries Malaysia (2018) https://www.dof. gov.
my/index.php/pages/view/3754 (accessed 24 Feb 2020)
Dunn DC, Stewart K, Bjorkland RH, Haughton M and
others (2010) A regional analysis of coastal and do -
mestic fishing effort in the wider Caribbean. Fish Res
102: 60−68
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ
(2011) A statistical explanation of MaxEnt for ecologists.
Divers Distrib 17: 43−57
Federal Register (2016) Fish and fish product import provi-
sions of the Marine Mammal Protection Act. https: // www.
mammal-protection-act (accessed 4 Dec 2019)
Gibbs MT, Browman HI (2015) Risk assessment and risk
management: a primer for marine scientists. ICES J Mar
Sci 72: 992−996
Goldsworthy SD, Page B (2007) A risk-assessment approach
to evaluating the significance of seal bycatch in two Aus-
tralian fisheries. Biol Conserv 139: 269−285
Grech A, Marsh H, Coles R (2008) A spatial assessment of
the risk to a mobile marine mammal from bycatch. Aquat
Conserv 18: 1127−1139
Gregr EJ, Baumgartner MF, Laidre KL, Palacios DM (2013)
Marine mammal habitat models come of age: the emer-
gence of ecological and management relevance. Endang
Species Res 22: 205−212
Hines E, Adulyanukosol K, Duffus D, Dearden P (2005)
Community perspectives and conservation needs for
dugongs (Dugong dugon) along the Andaman coast of
Thailand. Environ Manage 36: 654−664
Hines E, Reynolds J, Mignucci-Giannoni A, Aragones LV,
Marmontel M (eds) (2012) Sirenian conservation: issues
and strategies in developing countries. The University
Press of Florida, Gainesville, FL
Hines E, Ponnampalam LS, Hisne FIJ, Whitty TS, Jackson-
Ricketts J, Kuit SH, Acebes JM (2015a) Report of the 3rd
Southeast Asian Marine Mammal Symposium (SEAMAM
III). Langkawi Island, Malaysia, 4−10 March 2013. Con-
vention on Migratory Species of Wild Animals (CMS),
United Nations Environment Program. www. cms. int/ en/
publication/ report-third-southeast-asian-mar ine- mam
mals- symposium-seamam-iii (accessed 2 Dec 2019)
Hines E, Strindberg S, Jumchumpoo C, Ponnampalam L,
Ilangakoon A, Jackson-Ricketts J (2015b) Line transect
estimates of Irrawaddy dolphin abundance along the
eastern Gulf of Thailand. Front Mar Sci 2: 63
Hobday AJ, Smith ADM, Stobutzki IC, and others (2011)
Ecological risk assessment for the effects of fishing. Fish
Res 108: 372−384
Hoffman FO, Hammonds JS (1994) Propagation of uncer-
tainty in risk assessments: the need to distinguish be -
tween uncertainty due to lack of knowledge and uncer-
tainty due to variability. Risk Anal 14: 707−712
Jackson-Ricketts J (2016) Diet, life history, habitat, and con-
servation of Irrawaddy dolphins (Orcaella brevirostris) in
the Gulf of Thailand. PhD dissertation, University of Cal-
ifornia, Santa Cruz, CA
Endang Species Res 42: 37–57, 2020
Jackson-Ricketts J, Junchompoo C, Hines EM, Hazen EL,
Ponnampalam LS, Ilangakoon A, Monanunsap S (2020)
Habitat modeling of Irrawaddy dolphins (Orcaella brevi-
rostris) in the eastern Gulf of Thailand. Ecol Evol 10:
Jaramillo-Legorreta AM, Cardenas-Hinojosa G, Nieto-Gar-
cia E, Rojas-Bracho L and others (2019) Decline towards
extinction of Mexico’s vaquita porpoise (Phocoena sinus).
R Soc Open Sci 6: 190598
Johnson AF, Caillat M, Verutes GM, Peter C and others
(2017) Poor fisheries struggle with U.S. import rule. Sci-
ence 355: 1031−1032
Junchompoo C, Monanunsap S, Penpein C (2013) Population
and conservation status of Irrawaddy dolphins (Orcaella
brevirostris) in Trat Bay, Trat province, Thailand. Pro-
ceedings of the Design Symposium on Conservation of
Ecosystems. The 13th SEASTAR2000 workshop. Kyoto
University, Japan
Lewison RL, Crowder LB, Read A, Freeman S (2004) Under-
standing impacts of fisheries bycatch on marine mega -
fauna. Trends Ecol Evol 19: 598−604
Lewison RL, Soykan C, Franklin J (2009) Mapping the
bycatch seascape: multispecies and multi-scale spatial
patterns of fisheries bycatch. Ecol Appl 19: 920−930
Marsh H, Sobtzick S (2015) Dugong dugon. The IUCN Red
List of Threatened Species 2015: e.T6909A43792211.
http: // T6909
A 43792211.en. (accessed 4 Dec 2019)
Maxwell SM, Hazen EL, Bograd SJ, Halpern BS and others
(2013) Cumulative human impacts on marine predators.
Nat Commun 4: 2688
Minton G, Peter C, Tuen AA (2011) Distribution of small
cetaceans in the nearshore waters of Sarawak. Raffles
Bull Zool 59: 91−100
Minton G, Peter C, Zulkifli Poh AN, Ngeian J, Braulik G,
Hammond PS, Tuen AA (2013) Population estimates and
distribution patterns of Irrawaddy dolphins (Orcaella
brevirostris) and Indo-Pacific finless porpoise (Neopho-
caena phocaenoides) in the Kuching Bay, Sarawak. Raf-
fles Bull Zool 61: 877−888
Minton G, Smith BD, Braulik GT, Kreb D, Sutaria D, Reeves
RR (2017) Orcaella brevirostris (errata version published
in 2018). The IUCN Red List of Threatened Species 2017:
e.T15419A123790805. http: //
UK. 2017-3.RLTS.T15419A50367860.en. (accessed 4 Dec
Muscarella R, Galante PJ, Soley-Guardia M, and others
(2014) ENMeval: an R package for conducting cpatially
independent evaluations and estimating optimal model
complexity for Maxent ecological niche models. Methods
Ecol Evol 5: 1198−1205
Official Journal of the European Union (2019) Regulation
(EU) 2019/1241 of the European Parliament and of the
Council of 20 June 2019. https: //
content/EN/TXT/PDF/?uri=CELEX: 32019R1241 &from=
Ooi JLS, Kendrick GA, Van Niel KP, Affendi YA (2011)
Knowledge gaps in tropical Southeast Asian seagrass
systems. Estuar Coast Shelf Sci 92: 118−131
Ortega-Argueta A, Hines EM, Calvimontes J (2012) Using
interviews in sirenian research. In: Hines E, Reynolds J,
Mignucci-Giannoni A, Aragones LV, Marmontel M (eds)
Sirenian conservation: issues and strategies in developing
countries. The University Press of Florida, Gainesville, FL,
p 109–115
Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peter-
son A (2007) Predicting species distributions from small
numbers of occurrence records: a test case using cryptic
geckos in Madagascar. J Biogeogr 34: 102−117
Peter C, Zulkifli Poh AN, Ngeian J, Tuen AA, Minton G
(2016) Identifying habitat characteristics and critical
areas of Irrawaddy dolphin habitat (Orcaella brevirostris)
in Kuching Bay, Sarawak, Malaysia: implications for con-
servation. In: Das I, Tuen AA (eds) Naturalists, explorers
and field scientists in Southeast Asia and Australasia, Vol
15, Topics in biodiversity and conservation. Springer,
Philip GM, Watson DF (1982) A precise method for deter-
mining contoured surfaces. Australian Petroleum Explor
Assoc J 22: 205−212
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum
entropy modeling of species geographic distributions.
Ecol Modell 190: 231−259
Pilcher NJ, Adulyanukosol K, Das H, Davis P and others
(2017) A low-cost solution for documenting distribution
and abundance of endangered marine fauna and
impacts from fisheries. PLOS ONE 12: e0190021
Ponnampalam LS, Izmal JHF, Adulyanukosol K, Ooi JLS,
Reynolds JE (2015) Aligning conservation and research
priorities for proactive species and habitat management:
the case of dugongs Dugong dugon in Johor, Malaysia.
Oryx 49: 743−749
Read AJ, Drinker P, Northridge S (2006) Bycatch of marine
mammals in U.S. and global fisheries. Conserv Biol 20:
Redfern JV, Ferguson MC, Becker EA, Hyrenbach KD and
others (2006) Techniques for cetacean−habitat modeling.
Mar Ecol Prog Ser 310: 271−295
Reeves RR, Smith BD, Crespo EA, Notobartolo di Sciara G
(compilers) (2003) Dolphins, whales and porpoises:
2002− 2010. Conservation action plan for the world’s
cetaceans. IUCN/SSC Cetacean Specialist Group. IUCN,
Reeves RR, McClellan K, Werner TB (2013) Marine mammal
bycatch in gillnet and other entangling net fisheries,
1990 to 2011. Endang Species Res 20: 71−97
Rhoden CM, Peterman WE, Taylor CA (2017) Maxent-
directed field surveys identify new populations of nar-
rowly endemic habitat specialists. PeerJ 5: e3632
Roberts CM, McClean CJ, Veron JEN, and others (2002)
Marine biodiversity hotspots and conservation priorities
for tropical reefs. Science 295: 1280−1284
Samhouri JF, Levin PS (2012) Linking land- and sea-based
activities to risk in coastal ecosystems. Biol Conserv 145:
Sharp R, Tallis HT, Ricketts T, Guerry AD and others (2018)
InVEST 3.6.0 User’s Guide. The Natural Capital Project,
Stanford University, University of Minnesota, The Nature
Conservancy, andWorld Wildlife Fund. http: // data. natural html/
habitat_risk_assessment.html (accessed 2 Dec 2019)
Soykan CU, Moore JE, Žydelis R, Crowder LB, Safina C,
Lewison RL (2008) Why study bycatch? An introduction to
the Theme Section on fisheries bycatch. Endang Species
Res 5: 91−102
Stelzenmüller V, Fock HO, Gimpel A, Rambo H and others
(2015) Quantitative environmental risk assessments in
the context of marine spatial management: current ap -
proaches and some perspectives. ICES J Mar Sci 72:
Hines et al.: GIS-based bycatch risk assessment
Stewart KR, Lewison RL, Dunn DC, Bjorkland RH, Kelez S,
Halpin PN, Crowder LB (2010) Characterizing fishing effort
and spatial extent of coastal fisheries. PLOS ONE 5: e14451
Teck SJ, Halpern BS, Kappel CV, and others (2010) Using
expert judgement to estimate marine ecosystem vulnera-
bility in the California current. Ecol Appl 20: 1402−1416
Teh SLL, Teh CLL, Hines E, Junchumpoo C, Lewison RL
(2015) Contextualizing the coupled socio-ecological con-
ditions of marine megafauna bycatch. Ocean Coast
Manage 116: 449−465
Turvey ST, Pitman RL, Taylor BL, Barlow J, Akamatsu T and
others (2007) First human-caused extinction of a cetacean
species? Biol Lett 3: 537−540
Vu L (2014) Conservation status of cetaceans in Kien Giang
biosphere reserve, Kien Giang province, Vietnam. Final
report to Rufford Small Grant foundation. RSG reference
10664-1. Rufford Foundation, London
Williams R, Burgess MG, Ashe E, Gaines SD, Reeves RR
(2016) US seafood import restriction presents opportunity
and risk. Science 354: 1372−1374
Editorial responsibility: Jeremy Kiszka,
North Miami, Florida, USA
Submitted: December 22, 2019; Accepted: April 3, 2020
Proofs received from author(s): May 19, 2020
... Unfortunately, levels of mortality from bycatch of marine megafauna are still under-reported or unreported in many regions (Lewison et al., 2004a,b). This is a particular concern in low and middle-income countries that support high levels of marine biodiversity, but where documenting spatiotemporal patterns, rates and drivers of bycatch is often hindered by limited resources and capacity (Pilcher et al., 2017;Hines et al., 2020). ...
... Although there are increasing concerns about bycatch of megafauna in the SCS, most available information is anecdotal, with only limited local surveys having been conducted to date (Liu et al., 2019). Several initiatives have been developed to assess the extent of bycatch on marine mammals in the southern SCS, including Irrawaddy dolphins Orcaella brevirostris in the Philippines (Whitty, 2015(Whitty, , 2016 and Vietnam (Hines et al., 2020), as well as Indo-Pacific finless porpoises Neophocaena phocaenoides and Indo-Pacific humpback dolphins Sousa chinensis in Indonesia (Purnomo et al., 2015). In the northern SCS, Slooten et al. (2013) used photographic data to infer that >30 % of humpback dolphin individuals around Taiwan had net scars or injuries and found three individuals with fishing gear attached to their bodies, indicating that gillnet entanglement was likely to constitute a serious direct threat in this region. ...
... Information gaps on bycatch remain extensive for many biodiversityrich regions, as relatively few fisheries support onboard observer programmes to collect at-sea data on non-target catch (Kelleher, 2005), and many regions lack even anecdotal accounts of bycatch in the form of beach monitoring programmes or fisheries surveys (Hines et al., 2020). These factors contribute to the uneven geographic distribution of bycatch baselines, highlighting the need for additional monitoring and research projects, particularly in Global South countries. ...
Full-text available
There is still limited information available regarding the patterns and extent of bycatch mortality in heavily-fished marine systems. To address this gap, we conducted an interview-based study to investigate the bycatch of five marine megafaunal species/categories in the northern South China Sea. Approximately two-thirds of the interviewed fishers reported encountering bycatch events, with sea turtles being the most commonly reported megafauna (33.6 % of respondents), followed by Indo-Pacific finless porpoises and whale sharks (both 12.4 %), and Indo-Pacific humpback dolphins (8.5 %). Dugongs were only reported by one respondent. Bycatch of these taxa was mainly associated with gillnets, trawl nets, and seine nets. Our estimates indicated a total of 7464 bycatch events annually, resulting in 1391 deaths for these five taxa. These numbers underscore the alarming impact of fisheries on marine megafauna, particularly on small cetaceans, which accounted for 66.1 % of the reported annual deaths, including 690 finless porpoises and 230 humpback dolphins. Lower bycatch levels were reported for all taxa in winter, potentially due to temporary reductions in fishery activity and/or animal migrations. Random forest model revealed that bycatch of humpback dolphins, finless porpoises, and sea turtles was primarily influenced by water depth and distance from the coast, while whale shark bycatch was influenced by administrative region. This study demonstrates that local ecological knowledge can provide a rapid means of obtaining basic information on the bycatch of marine megafauna, and serves as a sobering reminder that fisheries-related mortality contributes to the population declines of coastal small cetaceans.
... Quoc and An Thoi, are of major importance to Vietnam's marine fisheries sector (FAO, 2005;Nguyen et al., 2013). In 2014, for example, the total catch registered in Kien Giang waters was 636,170 tons, which was equivalent to 20% of the total seafood landings in Vietnam (Hines et al., 2020). In addition to high-value fishes, a wide range of 'trash fish' are extracted for producing fish sauce, fishmeal, and fish oil, and this has turned traditionally lowvalue fishes into a commercial product (Nguyen, 2003). ...
... This infrastructure can reduce seagrass meadow coverage by causing meadow fragmentation and sedimentation, which can smother or reduce the light available to seagrasses (Unsworth et al., 2017;Evans et al., 2018). Trawling is commonly used in the east of Phu Quoc to maximize the by-catch of 'trash fish' (Bui et al., 2014;, but is highly destructive to seagrasses (Hines et al., 2020). The overall lower condition of fish communities in the PE region is likely to reflect these human impacts on seagrass habitat. ...
The Phu Quoc marine protected area (MPA) is a multiple‐use MPA that was established in 2007 and is a component of Vietnam’s National MPA System. The MPA is divided into two spatially separated zones based on habitat type: a seagrass zone and a coral reef zone. In this study, visual census data were collected in 2018–2019 and were used to derive fish biomass and community diversity metrics as proxies for ecosystem condition and function (trophic and mobility). The effectiveness of the MPA in protecting fish communities in the seagrass and coral reef zones was evaluated. An updated characterization of the Phu Quoc fish communities, and an assessment of fish community variation among regions and between habitat types, is provided. In total, 125 species representing 74 genera of 40 families were recorded. Omnivores were the most abundant (38%), followed by planktivores (24%), whereas piscivores (2%) were scarce. There was an especially low density of high‐value fishery target species such as Haemulidae (<0.01%), Carangidae (<0.2%), Lethrinidae (<0.3%), Lutjanidae (<0.4%), Serranidae (<2%), and Chaetodontidae (<3%). Alarmingly, fishes with a total length of >20 cm only accounted for 1% of the total individuals. Species and functional composition were similar between protected and unprotected areas but differed among regions and between habitats. These results suggest that the protection of fishes provided by Phu Quoc MPA at the time of this study was ineffective. The taxonomic composition of fish communities has remained relatively unchanged over the last approximately 25 years, with the density of large fishes and commercial species remaining low. There is capacity to improve MPA performance by providing adequate resources for threat management and using evidence‐based decision‐making in management. The 2020 rezoning and expansion of the Phu Quoc MPA is discussed with regards to prioritizing biodiversity and coverage targets.
... Redfern et al. 2013, Rockwood et al. 2018, Shearer et al. 2019) and entanglement in commercial fishing gear (NMFS 2020a, DFO 2019, Macks 2019,Hines et al. 2020).Barkaszi et al. (2021) provided estimates of risk from vessel traffic in Atlantic OSW WEA. ...
Technical Report
Full-text available
Offshore wind energy (OSW) development presents a range of potential stressors to North Atlantic right whales (Eubalaena glacialis; NARW), including increased ocean vessel traffic, noise, and habitat degradation. This report provides a summary of completed, ongoing, or planned NARW population monitoring studies, especially those related to OSW impacts; a synthesis of the methodologies used to gather the information needed to make marine mammal threat and impact assessments; an overview of responses from an online survey of representatives of the OSW energy industry, the NARW research community, environmental groups, and state and Federal agencies that provided information about current and planned research; descriptions of studies aimed at assessing NARW responses to OSW-related impacts and means to reduce the impacts; and recommendations for future action.
... Such data need to be representative of both fleet effort and bycatch rates, e.g. using randomized sampling stratification [8]. Estimates of marine mammal bycatch exist locally or regionally [9][10][11], but are frequently based on partial datasets of effort, bycatch and/or species distribution [12]. Bycatch data collection is often limited qualitatively (e.g. ...
Full-text available
Incidental captures (bycatch) remain a key global conservation threat for cetaceans. Bycatch of harbour porpoise Phocoena phocoena in set gillnets is routinely monitored in European Union fisheries, but generally relies on data collected at low spatio-temporal resolution or over short periods. In Denmark, a long-term monitoring programme started in 2010 using electronic monitoring to collect data on porpoise bycatch and gillnet fishing effort at a fine spatial and temporal scale, including time and position of each fishing operation, together with every associated bycatch event. We used these observations to model bycatch rates, given the operational and ecological characteristics of each haul observed in Danish waters. Data on fishing effort from the Danish and Swedish gillnet fleets were collected to predict fleet-wide porpoise bycatch in gillnets at regional level. Between 2010 and 2020, yearly total bycatch averaged 2088 animals (95% Cl: 667–6798). For the Western Baltic assessment unit, bycatch levels were above sustainability thresholds. These results demonstrate that fishing characteristics are key determinants of porpoise bycatch and that classical approaches ignoring these features would produce biased estimates. It emphasizes the need for efficient and informative monitoring methods to understand possible conservation impacts of marine mammal bycatch and to implement tailored mitigation techniques.
... These impacts may vary by species and population. Interview-based as sessments of bycatch rates and characteristics are widely considered the most cost-and time-effective method for estimating small-scale fisheries by catch and have been applied extensively (Moore et al. 2010, Pilcher et al. 2017, Whitty 2018, Hines et al. 2020. To complement these data, it will also be important to investigate the relative proportion of bycatch versus targeted catch. ...
Full-text available
River cetaceans are particularly vulnerable to anthropogenic impacts due to their constrained ranges in freshwater systems of China, South Asia, and South America. We undertook an exhaustive review of 280 peer-reviewed papers and grey literature reports (1998−2020) to examine the current status of knowledge regarding these cetaceans and their conservation. We aimed to better understand the scale of threats they face, and to identify and propose priority future efforts to better conserve these species. We found that the species have been studied with varying frequency and that most of the research on threats has focused on habitat degradation and fragmentation (43%, mainly driven by dams and extractive activities such as sand mining and deforestation), and fishery interactions (39%, in the form of bycatch and direct take). These threats occur across all species, but more information is needed, primarily on quantifying the population impacts as a basis for designing mitigation measures. Other threats identified include pollution, vessel collisions, traditional use, and poorly managed tourism. Emerging methods such as environmental DNA and unmanned aerial vehicles are described for studying these species. Promising conservation interventions include cetacean-specific protected areas, natural ex situ protection, community-led conservation, and education programmes. However, transnational political will is required for a step change towards broad-scale protection in freshwater environments. In addition, we propose in creasing capacity building, developing management plans, working closely with fishing communities, enhancing public awareness, expanding regional collaborations, and diversifying funding.
... This paper aims to build on this knowledge base and contribute to conservation and management through improved implementation of Cambodia's marine mammal stranding network. Beasley & Davidson (2007), Bohm (2019), and Hines et al. (2020) highlighted threats posed by illegal, unregulated and unreported (IUU) fi shing activity to marine mammals (notably Irrawaddy dolphins and dugongs), including bycatch, habitat degradation, and prey depletion. Cambodian fi sheries law prohibits the use of electrifi ed gears, gillnets and seine nets with a mesh size smaller than 1.5 cm, pair trawling nets, and bott om trawling at a depth less than 20 m (MAFF, 2007). ...
Full-text available
The Kep Archipelago in Cambodia supports a variety of ecologically important species, including the Endangered coastal Irrawaddy dolphin Orcaella brevirostris. This dolphin population has recently been subject to increased research, but faces growing threats from a variety of anthropogenic pressures, including pollution and illegal, unreported and unregulated fi shing activity. This study reports on the fatal strandings of ten Irrawaddy dolphins in Kep Province between 2017 and 2020 and documents the internal and external injuries recorded during rudimentary necropsies as well as the distribution, seasonality and demography of the stranding events. The strandings occurred throughout the archipelago in all seasons, although they were most prevalent during the post-monsoon season (October to November). Juveniles were most susceptible to stranding and no strandings of calves were recorded. The causes of death could not be accurately determined due to a lack of resources and trained personnel, although disease, chemical pollution and bycatch would appear to be the most likely causes of stranding. Observations of stomach contents confi rmed small bony fi sh, crustaceans and cephalopods as prey species. We recommend continual monitoring of Irrawaddy dolphin strandings along the Cambodian coastline, with a view to establishing a coastal-wide stranding network supported by adequate funding, resources, facilities and trained personnel such as marine mammal veterinarians. The information gathered from such a network would enhance understanding of the anatomy, physiology and pathology of Irrawaddy dolphins and inform conservation and management strategies for the species.
Full-text available
To understand the scope and scale of the loss of biodiversity, tools are required that can be applied in a standardized manner to all species globally, spanning realms from land to the open ocean. We used data from the International Union for the Conservation of Nature Red List to provide a synthesis of the conservation status and extinction risk of cetaceans. One in 4 cetacean species (26% of 92 species) was threatened with extinction (i.e., critically endangered, endangered, or vulnerable) and 11% were near threatened. Ten percent of cetacean species were data deficient, and we predicted that 2-3 of these species may also be threatened. The proportion of threatened cetaceans has increased: 15% in 1991, 19% in 2008, 26% in 2021. The assessed conservation status of 20% of species has worsened from 2008 to 2021, and only 3 moved into categories of lesser threat. Cetacean species with small geographic ranges were more likely to be listed as threatened than those with large ranges, and those that occur in freshwater (100% of species) and coastal (60% of species) habitats were under the greatest threat. Overlaying the species distribution maps revealed a global hotspots of threatened small cetaceans in Southeast Asia and in an area encompassing the Coral Triangle and extending through nearshore waters of the Bay of Bengal, northern Australia, Papua New Guinea, and into the coastal waters of China. Improved management of fisheries to limit overfishing and reduce bycatch is urgently needed to avoid extinctions or further declines, especially in coastal areas of Asia, Africa, and South America. This article is protected by copyright. All rights reserved.
Kuching Bay is a significant area for artisanal fishing activities as well as an Important Marine Mammal Area (IMMA) for coastal cetaceans. A total of 286 fishers from eight fishing communities were interviewed between 2011 and 2019 to determine the nature and extent of cetacean-fishery interactions in the area. The main types of fishing gears recorded were gillnets, trammel nets, trawl nets, longlines, handlines and crab traps, with the use of gears varying by season and target species. Depredation, net damage, and entanglements in fishing gear were the most frequently reported negative interactions with cetaceans. Thirty-six percent of fishers reported having experienced a cetacean entanglement in their fishing gear at least once. More than half (58.1%) of the respondents who experienced bycatch were able to disentangle and release the animals alive. The more conservative calculated bycatch rate of 0.36 cetaceans per fisher over a fishing career indicates that a minimum estimated average of 19 cetaceans are involved in bycatch annually in Kuching Bay, with as many as nine of these incidents likely resulting in mortality. However, a less conservative method yields a bycatch rate of 0.57 per fisher, and estimated an average of 30 bycaught cetaceans per year. Irrawaddy dolphins (Orcaella brevirostris) were reported to be at the highest risk (72.9% of reported incidents), with an estimated minimum of seven individuals caught and killed per year. Despite the negative interactions, 77.2% of respondents reported a generally positive attitude toward cetaceans based on their value for tourism and as indicators of fish presence and a healthy ecosystem. Mutualistic relationships between fishers and cetaceans were documented, with 53% of respondents reporting that they feed discarded fish to cetaceans. The results of this study can be used to guide effective mitigation measures, which should focus on training fishers in safe handling and release of entangled cetaceans, and, more importantly, methods to prevent interactions with gillnets.
Full-text available
The conservation status of small cetaceans has significantly worsened since the 1980s, when the baiji was the only species of small cetacean listed as Endangered by IUCN. Now the baiji is almost certainly extinct and 13 other species, subspecies, or populations (hereafter units-to-conserve or units) of small cetaceans are listed as Critically Endangered (CR) on the IUCN Red List. Bycatch is the main threat to 11 of the CR units. Entanglement in gillnets contributed to the extinction of the baiji and is responsible for the imminent extinction of the vaquita. Unfortunately, there is no simple technical solution to the problem of bycatch of small cetaceans. If the 8 CR units with 100 or fewer remaining individuals are to be saved, conservation zones must be established where gillnets are eliminated and bans on their use are strictly enforced. Recent experience with the vaquita in Mexico demonstrates that enforcement of such conservation zones can be very difficult. Ineffective enforcement is also a problem for at least 4 of the other CR units. Time is very short and, unless major efforts are made now to address the bycatch problem, the prospects for CR small cetaceans and other at-risk aquatic megafauna are grim. The ultimate long-term solution to the bycatch problem is the development of efficient, inexpensive, alternative fishing gear that can replace gillnets without jeopardizing the livelihoods of fishermen. Good fishery governance and the direct involvement of fishing communities are also essential to the successful conservation of most threatened populations of small cetaceans.
Full-text available
Fisheries bycatch is a widespread and serious issue that leads to declines of many important and threatened marine species. However, documenting the distribution, abundance, population trends and threats to sparse populations of marine species is often beyond the capacity of developing countries because such work is complex, time consuming and often extremely expensive. We have developed a flexible tool to document spatial distribution and population trends for dugongs and other marine species in the form of an interview questionnaire supported by a structured data upload sheet and a comprehensive project manual. Recognising the effort invested in getting interviewers to remote locations, the questionnaire is comprehensive, but low cost. The questionnaire has already been deployed in 18 countries across the Indo-Pacific region. Project teams spent an average of USD 5,000 per country and obtained large data sets on dugong distribution, trends, catch and bycatch, and threat overlaps. Findings indicated that >50% of respondents had never seen dugongs and that 20% had seen a single dugong in their lifetimes despite living and fishing in areas of known or suspected dugong habitat, suggesting that dugongs occurred in low numbers. Only 3% of respondents had seen mother and calf pairs, indicative of low reproductive output. Dugong hunting was still common in several countries. Gillnets and hook and line were the most common fishing gears, with the greatest mortality caused by gillnets. The questionnaire has also been used to study manatees in the Caribbean, coastal cetaceans along the eastern Gulf of Thailand and western Peninsular Malaysia, and river dolphins in Peru. This questionnaire is a powerful tool for studying distribution and relative abundance for marine species and fishery pressures, and determining potential conservation hotspot areas. We provide the questionnaire and supporting documents for open-access use by the scientific and conservation communities.
Full-text available
Background Rare or narrowly endemic organisms are difficult to monitor and conserve when their total distribution and habitat preferences are incompletely known. One method employed in determining distributions of these organisms is species distribution modeling (SDM). Methods Using two species of narrowly endemic burrowing crayfish species as our study organisms, we sought to ground validate Maxent, a commonly used program to conduct SDMs. We used fine scale (30 m) resolution rasters of pertinent habitat variables collected from historical museum records in 2014. We then ground validated the Maxent model in 2015 by randomly and equally sampling the output from the model. Results The Maxent models for both species of crayfish showed positive relationships between predicted relative occurrence rate and crayfish burrow abundance in both a Receiver Operating Characteristic and generalized linear model approach. The ground validation of Maxent led us to new populations and range extensions of both species of crayfish. Discussion We conclude that Maxent is a suitable tool for the discovery of new populations of narrowly endemic, rare habitat specialists and our technique may be used for other rare, endemic organisms.
Full-text available
IN THEIR POLICY Forum “U.S. seafood import restriction presents opportunity and risk” (16 December, p. 1372), R. Williams et al. describe some possible effects of the U.S. National Oceanic and Atmospheric Administration (NOAA) rule requiring that seafood imported into the United States must come from fisheries that comply with the U.S. Marine Mammal Protection Act (MMPA). Williams et al. point out that if fisheries are not adequately supported as they try to comply with the regulations, the rule could exacerbate difficulties experienced in poor fishing communities.....
Full-text available
Small boat surveys were conducted in the Kuching Bay area of Sarawak, East Malaysia, in order to determine the distribution and abundance of coastal cetaceans. Photographic data collected from Jul.2007 through Oct.2010 was used to generate mark-recapture abundance estimates of Irrawaddy dolphins in the study area, and provided insights into ranging patterns and site fidelity. Between Apr.2010 and Oct.2011, line transect surveys were conducted, and abundance estimates for Irrawaddy dolphins and Indo-Pacific finless porpoises were generated using distance sampling. The best mark-recapture estimate for Irrawaddy dolphins based on a weighted mean of estimates derived from photographs of left sides and right sides of dorsal fins was 233 (CV = 22.5%, 95% CI 151-360). Resighted individuals showed a high degree of site-fidelity, with less than 10 km between sighting locations over a period of four years for some individuals. A smaller proportion of re-sighted individuals ranged further-with a maximum straight-line distance of 26 km between sighting locations. The best line-transect estimate for Irrawaddy dolphins was 149 individuals (CV = 28%, 95% confidence interval 87-255). The line-transect estimate for finless porpoises was 135 individuals (CV = 31%, 95% confidence interval 74-246). Finless porpoise abundance varied seasonally, with higher densities observed between Mar. and May, coinciding with the occurrence of larger groups with very small calves. The line transect and mark-recapture derived estimates for Irrawaddy dolphins are compared, and viewed in the context of mapped relative densities that reveal key areas of habitat for the species. These abundance estimates provide a critical step toward the assessment of both species' local conservation status and can be used in the design of effective management strategies.
Full-text available
Effective conservation of coastal marine mammals is largely dependent on reliable knowledge of their abundance, as well as the ecological and human factors driving their distribution. In developing countries, lack of resources and capacity frequently impedes research needed to estimate abundance and to determine the ecological requirements of coastal marine mammals and the impact of threats related to coastal development and fisheries. Over the course of 5 years, we developed practical research methods and trained local scientists in Thailand to use accepted line transect distance sampling methods for abundance assessment. The study focused on a little-known coastal and freshwater species found throughout Southeast Asia, namely the Irrawaddy dolphin, which has been sighted regularly along the coast of the eastern Gulf of Thailand. During 5 years of line transect boat surveys in Trat Province, the eastern-most province in Thailand, we found an average of 423 dolphins distributed within 12 km of the coast. Compared to other abundance estimates of coastal Irrawaddy dolphins in Southeast Asia, this is a relatively large number. This population could extend into the northern coast of Cambodia, where surveys are currently being planned. The Thai government has begun talks with Cambodia about a transboundary marine protected area that would include areas in both countries where coastal Irrawaddy dolphins are found. Collaboration between scientists in Thailand, Cambodia and Vietnam is further needed to determine dolphin movement and habitat use across borders.
Full-text available
Risk assessment is the management approach or framework of choice in many disciplines, including health care and research, engineering design, and particularly the insurance sector which relies on the best available forward projections of natural hazards and accidents. The marine management community, which includes researchers, practitioners, and resource managers responsible for individual targeted stocks, aquaculture activities, and the marine environment in general, has been slower to take up quantitative risk assessment approaches. Whilst there are prominent examples where risk assessment and management approaches have been applied, they are relatively few. This article theme set presents examples of such and identifies tools and approaches that can be applied to coastal and oceanic marine systems worldwide. The methods developed and the lessons learned from these studies can be used to guide researchers, practitioners, and resource managers. It is hoped that this article theme set will provide an overview of the current state of risk assessment as applied to marine resource management, and stimulate new thinking on how risk assessment approaches can be applied.
On 1 January 2017, the U.S. National Oceanic and Atmospheric Administration (NOAA) will enact a new rule ( 1 ) requiring countries exporting seafood to the United States to demonstrate that their fisheries comply with the U.S. Marine Mammal Protection Act (MMPA). The United States is the world's largest seafood importer ( 2 ); the MMPA is among the world's strongest marine mammal protection laws; and most of the world's ∼125 marine mammal species are affected by fisheries bycatch (accidental entanglement in fishing gear) ( 3 ). This regulation could thus have significant conservation benefits, potentially spilling over to other areas of marine governance, if it is accompanied by substantial investments to boost scientific and compliance capacity in developing countries. Otherwise, it risks having little effect besides inflicting economic hardship on already poor communities.
Marine spatial planning (MSP) requires spatially explicit environmental risk assessment (ERA) frameworks with quantitative or probabilistic measures of risk, enabling an evaluation of spatial management scenarios. ERAs comprise the steps of risk identification, risk analysis, and risk evaluation. A review of ERAs in in the context of spatial management revealed a synonymous use of the concepts of risk, vulnerability and impact, a need to account for uncertainty and a lack of a clear link between risk analysis and risk evaluation. In a case study, we addressed some of the identified gaps and predicted the risk of changing the current state of benthic disturbance by bottom trawling due to future MSP measures in the German EEZ of the North Sea. We used a quantitative, dynamic, and spatially explicit approach where we combined a Bayesian belief network with GIS to showcase the steps of risk characterization, risk analysis, and risk evaluation. We distinguished 10 benthic communities and 6 international fishing fleets. The risk analysis produced spatially explicit estimates of benthic disturbance, which was computed as a ratio between relative local mortality by benthic trawling and the recovery potential after a trawl event. Results showed great differences in spatial patterns of benthic disturbance when accounting for different environmental impacts of the respective fleets. To illustrate a risk evaluation process, we simulated a spatial shift of the international effort of two beam trawl fleets, which are affected the most by future offshore wind development. The Bayesian belief network (BN) model was able to predict the proportion of the area where benthic disturbance likely increases. In conclusion, MSP processes should embed ERA frameworks which allow for the integration of multiple risk assessments and the quantification of related risks as well as uncertainties at a common spatial scale.