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

June 2020
Endangered Species Research 42

DOI:10.3354/esr01037

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Authors:

Ellen Hines

San Francisco State University

Louisa Ponnampalam

The MareCet Research Organization

Cindy Peter

University Malaysia Sarawak

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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.

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, SantubongSalak 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

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A reference table of uncertainty standards for Bycatch Risk Assessment (ByRA) criteria classification

…

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ENDANGERED SPECIES RESEARCH

Endang Species Res

Vol. 42: 37–57, 2020

https://doi.org/10.3354/esr01037 Published June 4

Attribution Licence. Use, distribution and reproduction are un -

restricted. Authors and original publication must be credited.

Publisher: Inter-Research · www.int-res.com

*Corresponding author: ehines@sfsu.edu

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

PEN

CCESS

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,

Vietnam

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

1. INTRODUCTION

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.

2014).

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

globally.

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-

talproject.stanford.edu/). 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. MATERIALS AND METHODS

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://

lawnet.sarawak.gov.my/lawnet_file/ 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

forestry.com/pdf/ 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

Kuching

Bay

Sibu-Tinggi

Islands

Kien Giang

0 250

Biosphere Reserve

500 Km

Trat Province

Kuching Bay

Kien Giang

Phu

Quoc

Biosphere Reserve

Island

02550Km

Sibu-Tinggi Islands

0510Km

Tra t Ba y

Ban Mai Rut

Khlong Yai

Besar I sland

Sibu Island

Tinggi Island

Mersing

Tan j un g

Leman

Bako-Bun tal

Bay

Santubong-Salak

Bay

Salak Telaga

Air Rivers

Santubong-Buntal

River

Ca Mau

Ha Tien

Son

Island

Nam Du

Island

Son Hai

Ba Lua

Islands

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 Quốc 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 Quốc 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

Quốc 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,

Malaysia

Kien Giang, Vietnam

ByRA species Irrawaddy dolphin

Orcaella brevirostris

Irrawaddy dolphin

Orcaella brevirostris

Dugong Dugong

dugon

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

temperature

(2014−2015) and

salinity (2015)

Animal survey

transects and/or

photo-ID

Boat surveys 2008, 2009,

2012−2016

Boat surveys 2008−2013 Aerial surveys 2010,

2014−2016; boat

surveys 2014-2016

Boat surveys

2014−2015

Sightings 1674 882 1360 4

Fishing occurrences Boat surveys 2008, 2009,

2012−2016

Boat surveys 2011−2013 Aerial surveys 2010,

2014−2016; boat

surveys 2014−2016

Boat surveys

2014−2015

Fishing areas Expert knowledge Expert knowledge Community interviews

2010, 2015−2017

Community inter-

views and expert

knowledge 2014,

2016

Interviews &

workshops

2008, 2009, 2012− 2015 2010−2012 2010, 2015−2017 2014, 2016

Protected areas Proposed National Parks/

RAMSAR site

Proposed Biosphere Reserve

Bycatch information Thai Department of

Marine and Coastal

Resource maps and

records/local stranding

network

No systematic reporting No systematic

reporting

No systematic

reporting

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

Surrounding

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

(shrimp/crab)

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

Khlong Yai

Zone 3

Zone 4

Zone 2

Zone 1

Top

Middle

Bottom

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. RESULTS

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

distribution

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

distribution

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

opinion

Sparse or incomplete data,

no geospatial or precise

locali zation of fishing

effort/gear

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

Rivers

Trawls

Hook & line

Nets

Pots & traps

Dry season

Nets

Trawlers

Pots & traps

Hook & line

Trawlers

Hook & line

Nets

Pots & traps

Trawls

Nets

Pots & traps

Longlines

Wet season

Trawlers

Longline

Hook & line

Nets

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 Quốc 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

Rivers

Santubong-Salak Bay Bako-Bun tal Bay

Santubong-Buntal River

Salak Telaga Air Rive rs

Santubong-Salak Bay

Santubong-Buntal

River

Bako-Buntal Bay

Santubong-Salak Bay

Salak Telaga Air

Rivers

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

coast.

3.2.4. Kien Giang Biosphere Reserve

The most suitable habitat for Irrawaddy dolphins

was along the coast, from the west of Phú Quốc

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 Quốc, 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 Quốc 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

Rivers

Santubong-Salak Bay Bako-Bun tal Bay

Santubong-Buntal River

Salak Telaga Air Rive rs

Santubong-Salak Bay

Santubong-Buntal

River

Bako-Buntal Bay

Santubong-Salak Bay

Salak Telaga Air

Rivers

Bycatch risk

Dry season

Low

Medium

High

Bycatch risk

Wet season

Low

Medium

High

Bycatch risk

Low

Fish landings

Medium

High

Bycatch risk

Low

Fish landings

Medium

High

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-

tribution

Systematic line transect

boat and photo-ID

survey

Systematic line transect

boat and photo-ID

survey

Systematic line

transect aerial survey

Systematic line transect

boat and photo-ID

survey but not

enough sightings to

characterize

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

survey

Environmental data

partially collected

Fishing

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/stranding

data

Bycatch determination

coming from necropsies

of stranded animals;

regional stranding

network

Presence/absence

of bycatch from

interviews

From interviews and

some records of

animals which had

stranded due to

fisheries interactions

Presence/absence

of bycatch from

interviews

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

trawlers.

4. DISCUSSION

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

Consequence

Trat Monsoon

3.0

2.5

1.5

1.0

3.0 2.0

Exposure

3.01.5 2.5

2.0

Consequence

3.0

2.5

1.5

1.0

3.0 2.0

Exposure

3.01.5 2.5

2.0

Consequence

3.0

2.5

1.5

1.0

3.0 2.0

Exposure

3.01.5 2.5

2.0

Consequence

3.0

2.5

1.5

1.0

3.0 2.0

Exposure

3.01.5 2.5

2.0

Consequence

3.0

2.5

1.5

1.0

3.0 2.0

Exposure

3.01.5 2.5

2.0

Consequence

3.0

2.5

1.5

1.0

3.0 2.0

Exposure

3.01.5 2.5

2.0

Consequence

3.0

2.5

1.5

1.0

3.0 2.0

Exposure

3.01.5 2.5

2.0

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

management.

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.
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Article

Jul 2022

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.

Offshore Wind Energy Development and North Atlantic Right Whales REPORT AVAILABILITY

Technical Report

Full-text available

Jul 2023

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.

Knowing the fishery to know the bycatch: bias-corrected estimates of harbour porpoise bycatch in gillnet fisheries

Article

Full-text available

Jul 2023

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.

Challenges and priorities for river cetacean conservation

Article

Full-text available

Sep 2022

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.

Irrawaddy dolphin (Orcaella brevirostris) strandings between 2017 and 2020 in Kep Province, Cambodia

Article

Full-text available

Jun 2022

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.

Spatiotemporal variations in the acoustic presence of dugongs and vessel traffic around Talibong Island, Thailand: Inputs for local coastal management from passive acoustical aspects

Article

Nov 2023
OCEAN COAST MANAGE

Red-list status and extinction risk of the world's whales, dolphins, and porpoises

Article

Full-text available

May 2023
CONSERV BIOL

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.

Utilizing interview-based data to measure interactions of artisanal fishing communities and cetacean populations in Kuching Bay, Sarawak, East Malaysia

Article

May 2023
OCEAN COAST MANAGE

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.

Utilizing Interview-Based Data to Measure Interactions of Artisanal Fishing Communities and Cetacean Populations in Kuching, Bay, Sarawak, East Malaysia

Article

Jan 2023

Bycatch in gillnet fisheries threatens Critically Endangered small cetaceans and other aquatic megafauna

Article

Full-text available

Dec 2019

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.

A low-cost solution for documenting distribution and abundance of endangered marine fauna and impacts from fisheries

Article

Full-text available

Dec 2017
PLOS ONE

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.

Maxent-directed field surveys identify new populations of narrowly endemic habitat specialists

Article

Full-text available

Jul 2017

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.

Poor fisheries struggle with U.S. import rule

Article

Full-text available

Mar 2017

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.....

Population estimates and distribution patterns of Irrawaddy dolphins (Orcaella brevirostris) and indo-pacific finless porpoises (Neophocaena phocaenoides) in the Kuching Bay, Sarawak

Article

Full-text available

Aug 2013
RAFFLES B ZOOL

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.

Line transect estimates of Irrawaddy dolphin abundance along the eastern Gulf Coast of Thailand

Article

Full-text available

Sep 2015

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.

Introduction: Risk assessment and risk management: A primer for marine scientists

Article

Full-text available

Mar 2015

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.

U.S. seafood import restriction presents opportunity and risk

Article

Dec 2016

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.

Contextualising the coupled socio-ecological conditions of marine megafauna bycatch

Article

Nov 2015

Quantitative environmental risk assessments in the context of marine spatial management: Current approaches and some perspectives

Article

Mar 2014

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.

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

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