Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin

Page 1
Integrated Analysis of Data
from MRC Fisheries Monitoring
Programmes in the Lower Mekong Basin
ISSN: 1683-1489
Mekong River Commission
MRC Technical Paper
No. 33
August 2013
C a m b o d i a
.
L a o P D R
.
Tha i l a n d
.
Viet N a m
For sustainable development

Integrated Analysis of Data
from MRC Fisheries Monitoring
Programmes in the Lower Mekong Basin
Mekong River Commission
MRC Technical Paper
No. 33
August 2013
Published in Phnom Penh, Cambodia in July 2013 by the Mekong River Commission
Cite this document as:
Halls, A.S.; Paxton, B.R.; Hall, N.; Hortle, K.G.; So, N.; Chea, T.; Chheng, P.; Putrea, S.; Lieng,
S.; Peng Bun, N.; Pengby, N.; Chan, S.; Vu, V.A.; Nguyen Nguyen, D.; Doan, V.T., Sinthavong, V.;
Douangkham, S.; Vannaxay, S.; Renu, S.; Suntornratana, U.; Tiwarat, T. and Boonsong, S. (2013).
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong

Basin. MRC Technical Paper No. 33, Mekong River Commission, Phnom Penh, Cambodia, 130pp.
ISSN: 1683-1489.
The opinions and interpretations expressed within are those of the authors and do not necessarily
reect the views of the Mekong River Commission.
Cover Photo: J. Garrison
Editors: T. Hacker, T.R. Meadley and P. Degen
Graphic design and layout: C. Chhut
Ofce of the Secretariat in Phnom Penh (OSP)
576 National Road, #2, Chak Angre Krom,
P.O. Box 623,
Phnom Penh, Cambodia
Tel. (855-23) 425 353
Fax. (855-23) 425 363
Ofce of the Secretariat in Vientiane (OSV)
Ofce of the Chief Executive Ofcer
184 Fa Ngoum Road, P.O. Box 6101,
Vientiane, Lao PDR
Tel. (856-21) 263 263
Fax. (856-21) 263 264
© Mekong River Commission
E-mail:
Website: www.mrcmekong.org
iii
Table of contents
List of Tables v
List of Figures vii
Abbreviations and Acronyms xi
Glossary of parameters xii
Glossary xiii
Acknowledgements xvi

Summary xvii
1. Introduction 1
1.1 Background 1
1.2 The aims of the paper 2
1.3 Relevance to FEVM Logframe Outputs and Activities 2
1.4 Structure of the paper 2
2 The Cambodian Dai Fishery Monitoring Programme 3
2.1 Introduction 3
2.2 Description of the shery 3
2.3 Monitoring programmes 5
2.4 Status and trends of resources 6
2.4.1 Catch composition 6
2.4.2 Trends in species composition and diversity 7
2.4.3 Trends in catch, indices of abundance and biomass and sh size (weight) 7
2.5 Hydrological inuences 10
2.6 Management effects 11
2.7 Conclusions 11
3. The Lao Lee Trap Fishery Monitoring Programme 13
3.1 Introduction 13
3.2 Description of the shery 13
3.3 Monitoring programmes 13
3.4 Status and trends 17
3.4.1 Catch composition 17
3.4.2 Trends in species composition and diversity 17
3.4.3 Inter-annual variation in relative biomass and hydrological effects 17
3.4.4 Intra-annual variation and hydrological effects 20
3.5 Conclusions 23
4. Fish Abundance and Diversity Monitoring Programmes (Small-Scale Artisanal Fisheries) 25
iv
4.1 Introduction 25

4.2 Monitoring programmes 25
4.3 Resource status and trends 30
4.3.1 Species composition 30
4.3.2 Trends in species diversity 31
4.3.3 Trends in relative sh abundance and biomass indices 36
4.3.4 Trends in growth 38
4.3.5 Abundance, biomass, growth and ooding 41
4.3.6 Other sites and species-wise analyses 41
4.4 Conclusions 41
5. Larvae Density Monitoring Programme 45
5.1 Introduction 45
5.2 Monitoring programmes 45
5.3 Status and trends 47
5.3.1 Species composition 47
5.3.2 Trends (all species) 49
5.3.3 Trends by species 52
5.4 Origin of ichthyoplankton drift 57
5.5 Summary and conclusions 57
6. Integrated analyses 61
6.1 Introduction 61
6.2 Methodology 62
6.2.1 The timing and extent of sh migrations 62
6.2.2 Spawning locations 62
6.2.3 Recruitment effects on stocks migrating from the TS-GL 63
6.2.4 Extent of sh migrations from the TS-GL 63
6.2.5 Extent of ood effects on sh growth 64
6.2.6 Management effects 64
6.2.7 Selected species 65
6.3 Results 66
6.3.1 Fish migrations 66

6.3.2 Spawning locations 75
6.3.3 Recruitment effects on stocks migrating from the TS-GL 78
6.3.4 Extent of sh migrations from the TS-GL 85
6.3.5 Extent of ood effects on sh growth 88
6.3.6 Management effects and recruitment 89
6.4 Summary and conclusions 91
7. Conclusions and recommendations 95
8. References 101
9. Annex 105
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page v
List of Tables
Table 1 Estimates of total annual catch 1938–1988 5
Table 2 Species composition of the catch for the 2009–10 shing season 6
Table 3 Sites monitored under both the AMCF and FEVM catch monitoring programmes
(2003–2010) 29
Table 4 Summary statistics for the catch monitoring programmes 30
Table 5 Annual trends in estimates of species richness indices 33
Table 6 Annual trend in estimates of relative abundance, biomass and mean body weight 38
Table 7 Estimates of geometric (GM) and arithmetic (AM) mean daily larvae density
at the Mekong and Tonle Sap sampling locations, Cambodia, between
June and September, 2002–2009 . 52
Table 8 Species exhibiting peaks in their larvae density estimates at the Mekong (MK)
and Tonle Sap (TS) sampling locations in 2005 and 2008 . 55
Table 9 Characteristics of species exhibiting peak larvae densities in 2005 and 2008
at the Tonle Sap and Mekong River sampling locations 56
Table 10 ANOVA results to test the dependence of log
e
-transformed larvae density (LNTS)
in the Tonle Sap on inowing volume of water (Q) accounting for differences among

species (SP) 59
Table 11 The species selected for the correlation/function analysis. 65
Table 12 Coefcients of linear regressions between estimates of larvae density in the Mekong River
at Phnom Penh 76
Table 13 Coefcients of linear regressions between estimates of larvae density in the Tonle Sap at
Phnom Penh and spawning stock biomass at the 10 locations 77
Table 14 Regression coefcients of the linear relationship between estimates of the arithmetic
mean daily larvae density-based recruitment index and log
e
-transformed dai catch rates 84
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page vi
Table 15 Regression coefcients of the linear dependence of relative spawning stock biomass
at locations in the LMB 86
Table 16 Regression coefcients of the linear dependence of relative abundance of spawning stock
size at locations in the LMB 87
Table 17 Regression coefcients of the linear dependence of mean body weight of species
sampled at sher catch monitoring locations in the basin during February each year
on the ood extent and duration in the TS-GL system 88
Table 18 Results of the GLM to test the dependence of dai catch rates on the quantity of fence 89
Table 19 Statistics of conscation and destruction of illegal shing gears (2000–2009). 105
Table 20 Percentage contributions of sh species to total catch weight reported under the
sher catch monitoring programmes, all gears and habitats 106
Table 21 Monthly estimates of catch weight (kg) at the sher catch monitoring
sites (2003–2010) 117
Table 22 Monthly estimates of the number of sh caught at the sher catch monitoring
sites (2003–2010) 118
Table 23 Monthly estimates of the mean weight (kg) of sh caught at the sher catch
monitoring sites (2003–2010) 119
Table 24 Monthly estimates of the number of species reported (S) at the sher catch

monitoring sites (2003–2010) 120
Table 25 Monthly estimates of the Margalef index at the sher catch monitoring sites
(2003–2010) 121
Table 26 Monthly estimates of the species richness index (SRI) at the sher catch monitoring sites
(2003–2010) 122
Table 27 Monthly estimates of sher catch rates (No/day) at the sher catch monitoring sites
(2003–2010) 123
Table 28 Monthly estimates of sher catch rates (kg/day) at the sher catch monitoring sites
(2003–2010) 124
Table 29 Pearson coefcients for correlations between average daily catch rates (kg/day)
by month at sher catch monitoring locations. 125
Page vii
List of gures
Figure 1 The stationary trawl or (Loh Dai) shery of the Tonle Sap-Great Lake (TS-GL) System,
Cambodia . 4
Figure 2 Illustration of the within-season variation in daily catch rates for the 2000–01 season 7
Figure 3 Mean log
e
-transformed dai catch rates (kg/dai/day) values by lunar phase for the seasons
1997–98 to 2008–09 8
Figure 4 Inter-annual trends in (a) total catch, (b) effort and (c) CPUE (1997–08 to 2009–10) 9
Figure 5 Trends in mean sh weight (all species combined) and the ood index 10
Figure 6 The Location of the Lee trap shery monitoring programme in southern Lao PDR 15
Figure 7 Species composition of the sampled catch from lee traps
in Hoo Som Yai Channel, 2009 17
Figure 8 The average multispecies assemblage catch rate in the Hoo Som Yai Channel,
1997–2009 18
Figure 9 Estimates of CPUE by sampling year for the 14 species monitored from 1997–2009. 19
Figure 10 Average multi-species catch rate in June each year (1997–2008) plotted as a function of
mean water level at Pakse in the same month 20

Figure 11 Mean daily lee trap CPUE for the multi-species assemblage migrating through
the HSY channel and water level measured at Pakse 21
Figure 12 Estimates of daily log
e
-transformed CPUE for the most frequently caught species
in the HSY in 2008 plotted as a function of water level at Pakse 22
Figure 13 The locations of the AMCF catch monitoring programme (2003–2005). 27
Figure14 The locations of the FEVM catch monitoring sites (2007–2010). 28
Figure 15 An example comparison of the log-linear relationship between monthly estimates of S
and sampling effort measured in terms of N and shing days (D) for the Banfang site,
Cambodia, monitored under the AMCF programme (2003–2005). 32
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page viii
Figure 16 Intra-annual (monthly) variation in the estimates of the Margalef and SRI
species richness indices at sites monitored under both the AMCF and FEVM
programmes 34
Figure 17 Monthly estimates of the Margalef and SRI species richness indices at sites monitored
under both the AMCF and FEVM programmes 35
Figure 18 Average monthly variation in the indices of sh abundance and biomass at the 10 sites
monitored under the AMCF and FEVM programmes (2003–2010). 37
Figure 19 Monthly estimates of the relative sh abundance and biomass indicated
by log
e
-transformed average sher catch rates per day at those sites monitored
under both the AMCF and FEVM programmes. 39
Figure 20 Average monthly variation in mean sh weight and monthly estimates of mean
sh weight at sites monitored under both the AMCF and FEVM programmes. 40
Figure 21 Estimates of indices of average sh abundance, biomass and mean weight in February
each year, plotted as a function of the ood index for the six sites in Cambodia 43
Figure 22 The location of the sh larvae density monitoring sites in Cambodia and Viet Nam 46

Figure 23 Species composition of larvae samples taken from the Tonle Sap and Mekong River
sampling sites 2002–2009 48
Figure 24 The number of species identied in larvae samples taken between June and September
each year plotted as a function of the number of sampling days during the same period. 49
Figure 25 Daily estimates of mean larvae density in the Tonle Sap and Mekong River, Cambodia,
2002–2009 50
Figure 26 Estimates of mean log
e
-transformed daily larvae density between June and September
for the Mekong river and Tonle Sap, 2002-2009 51
Figure 27 Estimates of arithmetic mean daily larvae density (June–September), 2002 – 2009,
all species. 52
Figure 28 Mean daily larvae density estimates (x 1000) between June and September, 2004–2009
for the Tonle Sap sampling site . 53
Figure 29 Mean daily larvae density estimates (x 1000) between June and September,
2004–2009 for the Mekong river sampling site 54
Page ix
Figure 30 Frequency of peak larvae density by year for species recorded in samples
from the Tonle Sap and Mekong River monitoring sites 54
Figure 31 Annual ood start, rise rate (FRR) at Kompong Luong, and duration
in the TS-GL system. 59
Figure 32 Annual maximum water level at Pakse, Lao PDR and Kompong Luong
in the Great Lake, Cambodia. 60
Figure 33 The generalized life-cycle and migration model for important whitesh species
in the LMB 61
Figure 34 Log
e
-transformed average monthly sher catch rates by site for Cirrhinus lobatus and
Henicorhynchus siamensis 69
Figure 35 Log

e
-transformed average monthly sher catch rates by site for Labeo chrysophekadion
and Puntioplites proctozystron 70
Figure 36 Log
e
-transformed average monthly sher catch rates by site for Cirrhinus microlepis
and Poropuntius malcolmi 71
Figure 37 Log
e
-transformed average monthly sher catch rates by site for
Cosmochilus harmandi and Yasuhikotakia modesta 72
Figure 38 Log
e
-transformed average monthly sher catch rates by site for Pangasius pleurotaenia
and Pangasius larnaudii 73
Figure 39 Log
e
-transformed average monthly sher catch rates by site for
Pangasius conchophilus and Hemibagrus nemurus 74
Figure 40 Estimates of the annual recruitment index, RI for the TS-GL system, 2002–2009
calculated as the product of the arithmetic mean (AM) or geometric mean (GM) daily
larvae density estimate, the mean daily inow of water and inow duration (days) 78
Figure 41 Log
e
-transformed dai catch rates (2004–05 to 2009–10) plotted as a function of
the annual recruitment index, RI estimated arithmetic mean daily larvae density and
geometric mean daily larvae density between June and September each year 79
Figure 42 Observed and predicted dai catch rates estimated as the product of the arithmetic (AM) or
geometric mean (GM) daily larvae densities (June to September), the mean daily ow into
the TS-GL system 80

List of Figures
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page x
Figure 43 Estimated larvae survival rates applied to the arithmetic mean larvae density to
generate the observed dai catch rates for the observed mean sh weights
between 2004 and 2009. 80
Figure 44 Predicted survival rates of larvae plotted as a power function of the larvae recruitment
index and equivalent instantaneous natural mortality rates plotted as a function of
log
e
-transformed recruitment index 81
Figure 45 Observed versus predicted dai catch rates. The upper gure shows catch rates predicted
using arithmetic mean larvae density estimates, the lower gure shows
the geometric-mean based equivalent 82
Figure 46 Log
e
-transformed dai CPUE plotted as a function of the arithmetic (top) and geometric
(bottom) mean daily larvae density-based recruitment index 83
Figure 47 Recruitment in year y+1 and spawning stock biomass (SSB) indicated by dai
catch rates in season y/y+1. 90
Figure 48 Recruits (RI) per spawner index plotted through time. 91
Figure 49 Mean R
2
value with S.E bars by size category for the species listed in Table 14 (except
Panagsius sp. 1–3) 93
Figure 50 Mean weight of sh caught by the dai shery plotted as a function of the TS-GL Flood
Index (FI) for (i) 2003–2004 to 2009–2010 94
Figure 51 Relative biomass of some important Mekong species on the IUCN list of endangered
species indicated by dai shery catch rates. 96
Page xi

Abbreviations and acronyms
AM Arithmetic Mean
AMCF Assessment of Mekong Capture Fisheries (Programme)
AMSL Above mean sea level
ANOVA Analysis of variance
CAS Catch Assessment Survey
CNY Chinese New Year
CPUE Catch per unit of effort
DDM Density-Dependent Mortality
DFMP Dai Fishery Monitoring Programme
DoF Department of Fisheries
FADMP Fish Abundance and Diversity Monitoring Programme
FEVM Fisheries Ecology Valuation and Mitigation
FI Flood Index
FiA Fisheries Administration
FLDMP Fish Larvae Density Monitoring Programme
GFL Great Fault Line
GLM General Linear Model
GM Geometric Mean
HSY Hoo Som Yai (channel)
IFRDC Inland Fisheries Research and Development Center
IFReDI Inland Fisheries Research and Development Institute
IUCN International Union for the Conservation of Nature
LARReC Living Aquatic Resources Research Center
LEK Local Ecological Knowledge
LMB Lower Mekong Basin
LTMP Lee Trap Monitoring Programme
MFCF Management of the Freshwater Capture Fisheries (Programme)
MFD Mekong Fish Database
MRC Mekong River Commission

MRCS Mekong River Commission Secretariat
RI Recruitment Index
RIA2 Research Institute for Aquaculture 2
SD Standard deviation
SE Standard error
SSB Spawning stock biomass
TSBR Tonle Sap Biosphere Reserve
TS-GL Tonle Sap-Great Lake
UNESCO United Nations Educational Scientic and Cultural Organisation
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page xii
Glossary of parameters
C Catch
CPUE Catch Per Unit of Effort
D Fishing days
F Instantaneous shing mortality rate
FENCE Quantity of fence gear conscated
FRR Flood rise rate (m day
-1
)
H Shannon Diversity Index
I
Margalef
Margalef’s Index
L
max
Maximum reported length of the species
LNTS Log
e
-transformed larvae density in the Tonle Sap (larvae m

-3
)
lp Lunar phase (1–4)
m Calendar month
n Sample size
N, n Fish abundance (number of sh) or number of sh in the sample or number of days
p Probability of committing a Type I or II Error
q Catchability coefcient
Q Flow (m
3
s
-1
)
r Dai row
r Correlation coefcient
R
2
Coefcient of determination
RI Recruitment Index
RIAMTSY Arithmetic mean recruitment index in year y
RI
y
The recruitment index in year y
RPS Recruits per spawner
S Species richness
S
max
Maximum species richness
SP Species code used in ANOVA
TL Total length

WL Water level
y Yield or year
Α, a Constant
Β, b Coefcient
Page xiii
Glossary
Analysis of variance Analysis of variance (ANOVA) is a collection of statistical models,
and their associated procedures, in which the observed variance in a
particular variable is partitioned into components attributable to different
sources of variation. In its simplest form ANOVA provides a statistical
test of whether or not the means of several groups are all equal, and
therefore generalizes t-test to more than two groups. Doing multiple two-
sample t-tests would result in an increased chance of committing a type
I error. For this reason, ANOVAs are useful in comparing two, three or
more means.*
Arithmetic mean The central tendency of a collection of numbers taken as the sum of the
numbers divided by the size of the collection*
Blacksh Species that possess morphological and physiological adaptations to
extreme environmental conditions including low dissolved oxygen
concentrations, and desiccation.
Catchability coefcient The proportion of the population removed by one unit of effort.
Coefcient A multiplicative factor in some term of an expression.
Coefcient of
determination
The coefcient of determination R
2
is used in the context of statistical
models whose main purpose is the prediction of future outcomes on the
basis of other related information. It is the proportion of variability in
a data set that is accounted for by the statistical model. It provides a

measure of how well future outcomes are likely to be predicted by the
model.*
Correlation Linear relationship between two variables, neither assumed to be
functionally dependent upon one another****.
Delury Depletion Model A method to estimate animal abundance by monitoring how indices of
abundance (e.g. catch rates) decline in response to cumulative shing
effort.*****
Flood Index A quantitative description of the extent and duration of ooding
corresponding to the area beneath and the area-duration curve above
mean ood levels.
Functional Dependence Functional dependence exists when the magnitude of one (dependent)
variable is determined by (is a function of) the magnitude of a second
(independent) variable****.
General Linear Model
(GLM)
The general linear model incorporates a number of different statistical
models.* In this document GLM provides a general version of multiple
linear regression where explanatory variables take the form of factors
and covariates.
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page xiv
Geometric mean The central tendency or typical value of a set of numbers. In this case
estimated as arithmetic mean of the natural logs of the numbers.
Lunar phase or (lunar
quarter)
Lunar quarters relate to four consecutive seven day periods starting from
the new (dark phase) moon. Quarter 2, when catch rates in the dai shery
are observed to peak, corresponds to the period of approximately 7–14
days after the new moon when between approximately 50–100 % of the
moon is visible. This period between what are commonly termed the rst

quarter and full moon phases is also known as the ‘Waxing Gibbous’
phase.
Multivariate (analysis) Analysis of multiple variables simultaneously.*
Pearson correlation
coefcient
A measure of the strength of the linear relationship between two
variables.*
Population dynamics Population dynamics is the branch of life sciences that studies short-term
and long-term changes in the size and age composition of populations,
and the biological and environmental processes inuencing those
changes. Population dynamics deals with the way populations are
affected by birth and death rates, and by immigration and emigration,
and studies topics such as ageing populations or population decline.*
Primary production Primary production is the production of organic compounds from
atmospheric or aquatic carbon dioxide, principally through the process of
photosynthesis, with chemosynthesis being much less important. Almost
all life on earth is directly or indirectly reliant on primary production.
The organisms responsible for primary production are known as primary
producers or autotrophs, and form the base of the food chain.*
Recruitment The number of sh (recruits) added to the exploitable stock, in the
shing area, each year, through a process of growth (i.e. the sh grows to
a size where it becomes catchable) or migration (i.e. the sh moves into
the shing area).**
Reophilic A preference to live in fast moving water.
Shannon Diversity Index The Shannon Diversity index, sometimes referred to as the Shannon-
Wiener Index is one of several diversity indices that can be used to
measure species diversity. The advantage of this index is that it takes into
account the number of species and the evenness of the species. The index
is increased either by having additional unique species, or by having a
greater species evenness.*

Species richness Species richness is the number of different species in a given area. It is
represented in equation form as S. Typically, species richness is used in
conservation studies to determine the sensitivity of ecosystems and their
resident species. The actual number of species calculated alone is largely
an arbitrary number.*
Page xv
Standard deviation Standard deviation shows how much variation or "dispersion" exists
from the average. A low standard deviation indicates that the data points
tend to be very close to the mean, whereas high standard deviation
indicates that the data points are spread out over a large range of values.
The standard deviation of a statistical population, data set, or probability
distribution is the square root of its variance.*
Standard error An estimate of that standard deviation, derived from a particular sample
used to compute the estimate*
Survey stratication The process of dividing members of the population into homogeneous
subgroups (stratum) before sampling to reduce sample variance.
Type I Error Falsely rejecting the null hypothesis when it is true.
Univariate (analysis) The analysis of a single variable.
Whitesh Migratory species intolerant of low dissolved oxygen conditions and
typically inhabit lotic (owing water) environments.
*
**
*** />**** Zar (1999)
***** Hilborn & Walters (1992)
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page xvi
Acknowledgements
The author is grateful to Norman Hall for their comments and suggestions that improved earlier
drafts of this report. Particular thanks to Bruce Paxton and Norman Hall for providing extensive
and carefully prepared materials and data for the lee trap and dry season gillnet sheries in southern

Lao PDR, and for the Cambodian dai shery - much of which forms the cornerstone of this report.
Many thanks to Ngor Pengby for helping compile and query the datasets for the dai shery and
sher catch monitoring programmes, and for preparing maps. Thanks also to Matti Kumi, Jorma
Koponen and John Forsius for their guidance and assistance in preparing the hydrological data.
The efforts of numerous people that have been involved in the design and implementation of the
monitoring programmes described here are gratefully acknowledged including the staff of the MRC
Fisheries Programme, Inland Fisheries Research and Development Institute (IFReDI), Cambodia,
Living Aquatic Resources Research Center (LARReC), Lao PDR, Inland Fisheries Research and
Development Center (IFRDC), Thailand, Research Institute for Aquaculture 2 (RIA2), Viet Nam, as
well as participating shers.
The preparation of this paper was facilitated by the MRC Fisheries Programme with funding from
DANIDA and SIDA.
Page xvii
Summary
Monitoring the status and trends of sheries resources in the lower Mekong basin (LMB) is required to
provide a baseline from which to monitor any impacts of sheries management and basin development
activities including dam construction.
Four major monitoring programmes have been supported by the Mekong River Commission to help
monitor the status and trends in the sheries in the lower Mekong basin:
1. The Dai Fishery Monitoring Programme (DFMP), Tonle Sap, Cambodia (1994–2010);
2. The Lee Trap Monitoring Programme (LTMP) at the Khone Falls, southern Lao PDR
(1994–2010);
3. The Fish Abundance and Diversity Monitoring Programme (FADMP) at up to 40
sites across the LMB (2003–2010); and
4. The Fish Larvae Density Monitoring Programme (FLDMP), Cambodia and Viet Nam
(1999–2010).
Analyses of much of the data generated by these programmes had been undertaken, some of which had
been published. However, only a limited amount of work has been done to construct time series of the
data collected that are much needed to interpret long-term trends in sh resources and for providing
baselines for impact monitoring purposes.

This report presents time series of indices of sh diversity, sh (and their larvae) abundance, biomass
and size for the multispecies assemblage and for important species estimated from data collected at
more than 50 locations in the LMB by these four monitoring programmes. Intra-annual variation and
long-term trends in these indices were examined.
Correlations and functional dependencies in the indices through space and time were also examined
and tested in an attempt to elucidate the extent of sh migrations and identify spawning locations in
the basin, and to improve understanding of the life-cycles and dynamics of sh stocks in the basin.
Signicant long-term (1997–2010) trends in the indices were not detected for the multispecies
assemblage that seasonally utilizes the Tonle Sap-Great Lake (TS-GL) system, nor were changes in its
species composition that might be attributable to increasing shing pressure in response to a growing
population. Even populations of some species that are included on the IUCN Red List of endangered
species and caught in the TS-GL system have shown no apparent decline in relative biomass.
Similarly, no signicant trend in the biomass of sh migrating upstream at the Khone Falls in southern
Lao PDR was detected between 1997 and 2009. Furthermore, no consistent trends in the indices of
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page xviii
relative abundance, biomass or species richness were observed among 10 sher catch monitoring
locations that have been monitored between 2003 and 2010. However, relative sh biomass at many
monitoring locations in the basin and reproductive success appears to have been relatively low since
2005–06 compared to earlier years. Signicant declines in the relative biomass index for several
species were also apparent at some locations, notably at Pres Bang on the Sekong River. The assertion
that the diversity and biomass of the multispecies assemblage have declined signicantly in the basin
therefore remains contentious. Much will hinge on whether recent estimates of relative sh biomass in
the system will recover to previous levels.
Most of the species selected for detailed assessment exhibit life cycles and migrations that are largely
consistent with the general life cycle model described by previous workers. Migrations of several
selected cyprinid and pangasiid catsh species appear to extend long distances upstream, at least as far
as the uppermost monitoring site at Luang Prabang. However, the migrations of other cyprinids and
pangasiid and bagrid catsh species appeared to be much more limited.
Fish migrations from the TS-GL system appear to be strongly linked to the lunar cycle as well as the

amount of water remaining on the oodplain. A lunar response of sh migrations was not detected
further upstream at the lee trap shery in southern Lao PDR. Instead water level appears to be an
important factor affecting the migrations of non-pangasiid species. The pangasiid catsh species
selected for monitoring here appeared to be caught in larger quantities at lower ows.
Statistical attempts to identify spawning locations in the LMB were largely unsuccessful but the
results of less formal analyses suggest that among others, Stung Treng province, Cambodia and the
three tributaries in the Sesan basin are relatively important spawning locations for small cyprinids. The
Srepok River also appears to provide important habitats for medium and large species of cyprinid and
the Sesan and Sekong rivers also appeared to provide important habitats for pangasiid catsh. These
locations are consistent with results of analyses of age distributions of larvae sampled at Phnom Penh.
Thus, greater consideration might be given to conserving tributary habitat during basin development
planning in the future but more research is also needed to fully understand the role of tributaries in the
LMB in the lifecycles of important species.
The abundance and biomass of the multispecies assemblage that seasonally utilises the TS-GL system
responds signicantly to the transport of larvae from upstream spawning locations and the extent and
duration of ooding indicated by the ood index (FI). It appears that record catch rates recorded for
the dai shery in 2004–05 and 2005–06, and apparent elsewhere in the basin in 2005 were in response
to very high rates of recruitment during 2004 and 2005, rather than growth effects.
These high levels of recruitment could not be linked to management efforts to conserve or rebuild
spawning stock biomass by conscating illegal gear in the TS-GL system. Rather, it appears that a
combination of spawning success, larvae survival and rates of transport were important.
Water levels rose rapidly in 2005, second only to the rates observed in 2002. This may have stimulated
upstream spawning migrations and beneted larvae survival and transport. However, reasons for the
very high rates of recruitment estimated for Henicorhynchus species in 2004 remain perplexing. A
Page xix
Summary
closer examination of hydrological and water quality parameters across the geographic range of these
species and particularly during the spawning season at likely spawning locations, including the Sesan
basin, appears warranted.
Many of the analyses described in this document were hampered by the low precision of index

estimates. Therefore, consideration might be given to reviewing the size of samples taken by each
monitoring programme to detect acceptable minimum detectable differences in index estimates. Other
recommendations to improve the four monitoring programmes and their databases are described.
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page xx
Page 1
1. Introduction
1.1. Background
Four major monitoring programmes have been supported by the Mekong River Commission to help
monitor the status and trends in the sheries in the lower Mekong basin:
1. The Dai Fishery Monitoring Programme (DFMP), Tonle Sap, Cambodia
(1994–2010);
2. The Lee Trap Monitoring Programme (LTMP) at the Khone Falls, Lao PDR
(1994–2010);
3. The Fish Abundance and Diversity Monitoring Programme (FADMP) at up to
40 sites across the LMB (2003–2010); and
4. The Fish Larvae Density Monitoring Programme (FLDMP), Cambodia and Viet Nam
(1999–2010).
Since December 2007, the FADMP included two additional monitoring locations (Hat and
Hadsalao villages) in southern Lao PDR. Between 1994 and 2007 catch rates of between 5 and 10
gillnet shers were monitored at these two locations for between 13 and 15 days during the dry season
each year around the time of the Chinese New Year (CNY). Because the CNY falls on a different
calendar day each year depending on the moon phase, the monitoring period has changed each year
between 20th January and 2nd March (Paxton, undated). Since December 2007, catch rates at these
sites have been monitored daily under the FADMP.
As well as providing a means to monitor the status and trends of sheries resources in the basin,
these programmes also provide a baseline from which to monitor any impacts of sheries management
and basin development activities including dam construction.
Detailed analysis of the data generated by DFMP programme have been reported
(Halls et al., 2011). Preliminary analyses of the data collected under the LTMP and dry season

gillnet shery monitoring in southern Lao PDR have also been undertaken (Paxton, undated)
and reported by MRC (2010). Whilst analyses of data generated by the FADMP and the FLDMP have
been reported (e.g. Doan et al., 2006; Nguyen et al., 2006 and Hortle et al., 2005), less work had been
done to construct time series of the data collected. Such series are not only important for assessing
and interpreting trends in indices of larvae and sh abundance and diversity but also form important
baselines for impact monitoring purposes.
Integrated Analysis of Data from MRC Fisheries Monitoring Programmes in the Lower Mekong Basin
Page 2
Moreover, comparing variation in these indicators among monitoring sites can potentially provide
information on the spatial extent of stocks as well as the extent over which resources respond in a
similar manner to inter-annual variation in environmental conditions including hydrology. Comparing
time series from these four monitoring programmes in a more integrated manner assists
the interpretation of their respective trends.
1.2 The aims of the paper
This paper aims to describe the status and trends of sheries resources at monitoring locations across
the Lower Mekong basin. It also explores the spatial and temporal dynamics of widely abundant
species in an attempt to improve knowledge and understanding of sheries resources in the LMB
for management and basin development planning purposes. Catch rate data collected at Hat and
Hadsalo villages prior to 2007 have not been included here because of the short and variable timing of
monitoring activities at these locations, making it difcult to draw valid comparisons with data from
the other surveys. Catch rate trends for Hadsalo are illustrated in MRC (2010).
1.3 Relevance to FEVM Logframe Outputs and Activities
The research will contribute to FEVM Logframe Outputs 1, 3 and 5: Improved information on the
ecology of the sheries of the LMB and models for basin planning purposes are available to basin
planners and development agencies. Improved institutional capacity to monitor and evaluate sheries
resource status and trends. FEVM Logframe Activities addressed are: 1.2 Describe trends in sh
abundance and diversity in the basin; 3.3 Develop models to predict the effects of modied ows on
sheries; Train national agencies in sheries assessment methods.
1.4 Structure of the paper
The following Sections 2 to 5 contain descriptions of the four monitoring programmes and where

relevant the sheries they sample. Important species sampled by each programme are identied.
Inter-and intra-annual variations in indices of diversity, abundance, and where relevant, biomass
and sh size, are examined. For the FADMP, monthly estimates of each index for each monitoring
location are tabulated in the Annex (Table 21–Table 28). Section 6 draws upon data collected under
each monitoring programme. It seeks correlations and functional dependencies in the indices through
space and time in an attempt to reveal the extent of sh migrations and the location of spawning areas
in the basin, as well as to improve understanding of the life-cycles and dynamics of sh stocks in the
basin. Each section ends with a summary and conclusions. Overall study conclusions are drawn and
recommendations made in Section 7.
Page 3
2 The Cambodian Dai Fishery Monitoring Programme
1
2.1 Introduction
The dai shery on the Tonle Sap River, which was established almost 140 years ago, is an important
component of the Tonle Sap Great Lake (TS-GL) shery, targeting the migrations of a multi-species
assemblage of sh that migrates from the Great Lake to the Mekong main channel with the receding
oodwaters each year.
The dai shery contributes up to 33,000 tonnes or 7 % of Cambodia’s total annual landings of sh
from the Mekong Basin estimated to be in the region of 480,000 tonnes per annum (Hortle, 2007),
valued at more than US$6 million in 2006. The remaining proportion of the country’s total catch is
taken using large-scale fence and barrage traps, and by small and middle-scale sheries employing
seines, gillnets, small trawls, bamboo traps, cast nets and hook and line (Baran, 2005).
The dai shery provides important seasonal employment opportunities for more than 2,000 rural
people and supplies the essential ingredient for prahock, a fermented paste which is an important
protein source for many, particularly towards the end of the dry season when sh is scarce
(Halls et al., 2007).
2.2 Description of the shery
The shery is located in the lower section of the Tonle Sap River spanning more than 30 km across the
municipality of Phnom Penh and Kandal Province (Figure 1). Dai nets (stationary trawls) are arranged
in up to 15 separate rows of between one and seven nets anchored perpendicularly to the channel, with

the net mouths facing upstream. The most upstream Row (15) is located approximately 35 km from
Phnom Penh. These positions have remained largely unchanged for more than a century and may have
been chosen to maximize catch rates determined by river morphology and hydrology.
The dai shery primarily targets small cyprinids of the Cirrhinus, Labiobarbus and
Henicorhynchus genera, collectively known as trey riel in Khmer. Other species making an
important contribution to landings are pelagic river carp (Paralaubuca barroni), and species of
loach. The families Cyprinidae and Cobitidae dominate the landings. Their species are targeted as
they migrate from the TS-GL system and surrounding oodplains to what are hypothesized to be dry
season refuge habitat (e.g. deep pools) in the main channel as water levels fall between October and
March. As water levels begin to rise at the start of the next wet season (April–May), it is hypothesized
that adults migrate upstream to spawn and then return back downstream with their larvae and juvenile
progeny to the TS-GL system and other oodplain feeding habitat. Further details of the operations
and management of the shery are described by Halls et al. (2011).
1
This section draws heavily from Halls et al., (2011); Halls and Paxton, (2010).