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TYPE Original Research
PUBLISHED 06 August 2024
DOI 10.3389/wsc.2024.1426350
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Ghent University, Belgium
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US Geological Survey and Mississippi State
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Senckenberg Research Institute and Natural
History Museum Frankfurt, Germany
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Ratha Sor
sorsim.ratha@gmail.com
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PUBLISHED 06 August 2024
CITATION
Sor R, Prudencio L, Hogan ZS, Chandra S,
Ngor PB and Null SE (2024) Factors
influencing fish migration in one of the
world’s largest inland fisheries.
Front. Freshw. Sci. 2:1426350.
doi: 10.3389/wsc.2024.1426350
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which does not comply with these terms.
Factors influencing fish migration
in one of the world’s largest
inland fisheries
Ratha Sor1,2,3*, Liana Prudencio1, Zeb S. Hogan4,
Sudeep Chandra4, Peng Bun Ngor5and Sarah E. Null1
1Department of Watershed Sciences, Utah State University, Logan, UT, United States, 2Postgraduate
School, National University of Cheasim Kamchaymear, Phnom Penh, Cambodia, 3Wonders of the
Mekong Project, Phnom Penh, Cambodia, 4Global Water Center and Department of Biology, University
of Nevada, Reno, NV, United States, 5Faculty of Fisheries, Royal University of Agriculture, Phnom Penh,
Cambodia
Fish from Cambodia’s Tonle Sap Lake are economically, culturally, and
nutritionally significant for people in the Lower Mekong Basin, providing income,
livelihoods, and protein. Fish in this system generally migrate toward upstream
Mekong River in dry season and return in early wet season. However, drivers
of fish migration from Tonle Sap Lake to the Mekong River are not well-
understood. In this paper, we utilized Mixed Eects Random Forest to predict
the catch weight of six fish species migrating from the Tonle Sap Lake to the
Mekong River using precipitation, lunar cycle, and hydrologic conditions like
river stage, streamflow, flow magnitude, and timing as predictors. As a surrogate
for fish migration, we used daily fish catch weight from 2002 through 2008
at the bagnet, or Dai, fisheries along Tonle Sap River, a migration corridor
connecting Tonle Sap Lake to the Mekong River. We found that migration of
large fish was mainly cued by streamflow and flow magnitude, while smaller
fish migrate depending on the combination of streamflow and flow timing.
Streamflow less than average cumulative flow was the most important driver
for migration of Pangasianodon hypophthalmus, and Cirrhinus microlepis.
Migration of Cyclocheilichthys enoplos and Osteochilus melanopleurus was
highly dependent on the number of low- and minimum-flow days. Cumulative
flows, period of high flow and water level were the main predictors of the
small mud-carp Henicorhynchus entmema’s migration, while individuals of
Labiobarbus leptocheilus migrated out of the Tonle Sap Lake depending on
the number of days after 7-, 30-, and 90-day minimum flows. These results
suggest that flow characteristics can be used to aid conservation and adaptive
management of Cambodia’s Dai fisheries.
KEYWORDS
Tonle Sap Lake, Mekong River, flow, duration, timing, Dai fisheries, adaptive
management
1 Introduction
Inland fisheries have broad and wide-ranging benefits, supporting individuals,
societies, environmental functions, and ecosystem services (Lynch et al., 2016). In low-
income countries, inland fisheries are especially important because they provide livelihoods
for over 60 million people (FAO, 2014). However, inland fisheries are threatened by
biological invasion (FAO, 2014), overfishing (Ngor et al., 2018c), and dam development
(Barbarossa et al., 2020). Existing dams have already reduced fish biodiversity and catches
in the Lower Mekong Basin (Sor et al., 2023), while the range of fish species migration is
likely to be reduced in other tropical rivers due to future dams (Barbarossa et al., 2020).
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Sor et al. 10.3389/wsc.2024.1426350
In the Lower Mekong Basin, freshwater fish are economically
and culturally important, providing food security to ∼65 million
people (Mekong River Commission, 2019). Hotspots of fish
diversity and biomass include Cambodia’s Tonle Sap Lake and three
Mekong River tributaries formed by the Se Kong, Se San, and Sre
Pok Rivers, collectively called the 3S Basin (Ngor et al., 2018a;
Sor et al., 2023). An industrial bagnet, or “Dai,” fishery as it is
locally known, in Cambodia’s Tonle Sap River yields an average of
∼12,000 tons of fish each year (Baran, 2006). The Dai fishery has
been operating for more than 140 years, and relies on the unique
hydrology of the Tonle Sap system (Halls et al., 2013). In the dry
season from October to May, fish migrate from Tonle Sap Lake
to spawning grounds and dry season refuges in the Mekong River
and upstream tributaries (Baird et al., 2004). The Dai closes in
the wet season when flow of the Tonle Sap River reverses, flowing
from the Mekong River into Tonle Sap Lake and providing crucial
habitat for fish feeding and rearing (Chan et al., 1999;Halls et al.,
2013). This flow reversal increases Tonle Sap Lake’s area from
roughly 2,600 km2in the dry season to about 15,000 km2in the
wet season (Cochrane et al., 2014), expanding to about one and
a half times larger than Lake Erie in the United States. The wet
and dry seasons of the Mekong Basin support unparalleled aquatic
biodiversity (Sor et al., 2014,2017;Ngor et al., 2018a;Tudesque
et al., 2019).
Recent and ongoing hydropower dam construction throughout
the basin is homogenizing flows, creating drier wet seasons and
wetter dry seasons (Hecht et al., 2019;Null et al., 2021;Chann
et al., 2022), changing water quality and habitats (Lohani et al.,
2020;Sor et al., 2021), and threatening fishes that rely on the annual
flood-pulse (Ngor et al., 2018b;Null et al., 2021). Proposed future
dams in the Lower Mekong Basin could further alter hydrology and
ecosystems (Arias et al., 2014;Morovati et al., 2023). For instance,
fish biomass and diversity have decreased in Mekong tributaries
that have the highest concentrations of dams, while biomass has
increased in free-flowing rivers or those with few dams because
they offer the last remnants of connected habitats (Sor et al., 2023).
Proposed dams threaten the remaining migration corridor between
Tonle Sap Lake, the mainstem Mekong River, and the Sekong River
(Null et al., 2021;Sor et al., 2023). Intense and indiscriminate
fishing also threatens fish in the Tonle Sap system (Ngor et al.,
2018c). While total catch has remained stable over time, fast-
growing, small fish have replaced larger fish due to overfishing
(Ngor et al., 2018c). Lower diversity of fishes leads to ecosystems
that may be less resilient to changing environmental conditions
from climate change, water development, land use change, and
other anthropogenic alterations.
Identifying daily migration triggers for native fish species is
important to understand how existing and future dams, drought
intensification, and land use changes could alter streamflow
components in the Tonle Sap River and disrupt fish migrations. The
indicators of hydrologic variation approach (Richter et al., 1997)
and, more conceptually, the natural flow paradigm (Poff, 1997),
assume that streamflow is a master variable for ecological function.
Streamflow can be characterized by magnitude, frequency, timing,
duration, and rate of change to understand hydrologic components
that cue fish to migrate (Richter et al., 1997). Similarly, precipitation
and lunar cycle are physical drivers that may drive fish movement.
In the Lower Mekong Basin, previous research has linked fish
migration with discharge, water level, rainfall, and lunar cycle
(Baran, 2006). In the Tonle Sap system, flood extent and duration
(Halls et al., 2013) and flood pulse extent and hydrologic variance
(Sabo et al., 2017) have been correlated with fish biomass, as
indicated by Dai catch per unit effort. However, few studies
have evaluated hydrologic predictors for individual species. One
of the studies that we identified illustrated that water level is
significantly correlated with daily catch of the small mud carp
Henicorhynchus entmema (Chan et al., 2019) and another reported
correlation between water levels and catch of juvenile striped catfish
(Pangasianodon hypophthalmus;Chhuoy et al., 2022). However,
these studies did not evaluate which hydrological and physical
predictors cue fish migration.
Our objective is to test whether lunar cycle, precipitation,
and streamflow conditions cue six fish species to migrate from
Cambodia’s Tonle Sap Lake to the Mekong River. Our fish species
included a large river catfish (Pangasianodon hypophthalmus),
three medium-sized carp (Cyclocheilichthys enoplos,Osteochilus
melanopleurus, and Cirrhinus microlepis), a keystone species mud
carp (Henicorhynchus entmema), and a highly abundant mud carp
(Labiobarbus leptocheilus). The latter two species are particularly
important as local food sources. These species are representative of
ecologically and economically important fish in the Lower Mekong
Basin. We expected that the migration of these fish species are
influenced by river flow, water level, precipitation, and lunar cycle,
as found in the Lower Mekong Basin (Baird and Flaherty, 2001;
Baran, 2006).
2 Materials and methods
2.1 Study area and fish data
The Tonle Sap system includes Tonle Sap Lake and River
(Figure 1A). The Tonle Sap system is well-known for rich aquatic
fauna biodiversity, ranging from invertebrates such as rotifers
(Sor et al., 2014), aquatic insects (Sor et al., 2017;Chhorn et al.,
2020;Doeurk et al., 2022), annelids and crustaceans (Sor et al.,
2017), and molluscs (Sor et al., 2017,2020) to fish (So et al.,
2018). Most migratory fish belong to two families, Cyprinidae
and Pangasiidae (Ngor et al., 2018b;Sor et al., 2023). The
species of these families are diverse and make up ∼82% of
the fish catch in Cambodia, and their catch weight has been
declining at the Dai fishery over the last 15 years (Ngor et al.,
2018c).
We used daily catch per unit effort (CPUE) of six fish species
collected from the Dai fishery in the Tonle Sap River from
2002 to 2008. Five species (all except P. hypophthalmus) belong
to Cyprinidae, the family of fishes most widely caught in this
system, while P. hypophthalmus belongs to the catfish family,
Pangasiidae, which are also widely caught. The five cyprinid species
are economically and ecologically important, as they are keystone
species, prey for predatory fish and Irrawaddy dolphins (Orcaella
brevirostris), and are widely sold in fish markets (Fukushima et al.,
2014;Ngor et al., 2018c). The species H. entmema is the most
harvested species in the Dai fishery (Ngor et al., 2018c). The giant
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FIGURE 1
(A) The Tonle Sap River flows from Tonle Sap Lake to the Mekong River in the dry season, and from the Mekong River toward Tonle Sap Lake in the
wet season. Gray arrows indicate the direction of the Mekong River. The red arrow indicates the bi-directional Tonle Sap River. The black line is
Cambodia’s boundary. (B) The Dai (bagnet) fishery on the Tonle Sap River (yellow circles) is made up of 64 units across 14 rows.
striped-catfish P. hypophthalmus is important as a food source,
income for local people, and regulates aquatic fauna diversity (e.g.,
mollusc and smaller fish; Ngor et al., 2018c;Sor et al., 2020).
The Dai fishery operates from October to March, which is the
tail end of the wet season through much of the dry season. Peak
catch usually occurs in December and January. The Dai fishery
comprises a total of 64 Dai units belonging to 14 Dai rows along
the Tonle Sap River, and they cover ∼30 km from the first to
the final row (Figure 1B). The mouth of each Dai unit is ∼25 m
(Supplementary Figure 1), with mesh size ranging from ∼15 cm at
the mouth to 1 cm at the cod end. Nets face upstream to catch fish
migrating from Tonle Sap Lake to the Mekong River (Halls et al.,
2013).
The 2002 to 2008 fish sampling effort followed a random
stratification of the Dai units, based on location of the Dai rows
and units (high vs. low catch units), and the peak and low
catch periods. Fish catch and composition was analyzed for each
selected Dai unit. Fishing took place up to 17 days/month, with
daily sampling during the peak period and sampling every 2nd
or 3rd day in the low catch period. During peak catch, nets
were hauled every 15–30 min, with 48–96 hauls per day, and
catch was weighed and identified to species. During low catch
periods, nets were hauled every 2–3 h, or 8–12 hauls per day.
Then, Catch Per Unit Effort (CPUE), a daily catch rate of the
Dai unit (kg), was estimated as the product of sampled weight
for every haul sampled per day, and the average daily CPUE
was calculated using the mean daily catch per haul multiplying
by the total number of hauls per day. Further detail on the
sampling procedure and CPUE computation is in Ngor et al.
(2018c).
The maximum total length, the length from the tip of
snout to the tip of depressed caudal fin, and catch weight
characteristics for the six study species are provided in Table 1.
Catch weight time-series, log-transformed catch weight time-series
(natural logarithm), catch weight distribution and log-transformed
catch weight distribution (natural logarithm) for each species are
provided in Supplementary Figures 2–7.
We assume fish migration from Tonle Sap Lake to the Mekong
River is represented by fish daily catch weight from the Dai
(i.e., CPUE) because the stationary bagnets are designed to catch
migratory fish. Each Dai unit can capture up to 2.8% of migrating
fish. From the first to the final row, an estimated 83% of migrating
fish are collectively caught in Tonle Sap River bagnet fishery (Halls
et al., 2013;Ngor et al., 2018c). Note that, catch is generally highest
in the first few Dai rows, and gradually decreased until the final row.
2.2 Environmental predictors
Environmental predictors used in this study are streamflow
(three predictors), flow magnitude (seven predictors), flow timing
(10 predictors), flow rate of change (one predictor), precipitation
(one predictor), and lunar cycle (one predictor; Table 2). Flow
characteristics, including magnitude, timing, and rate of change,
are hydrologic components which drive ecosystem function and
support freshwater biodiversity, habitat, and ecosystem services
(Richter et al., 1996). For example, the magnitude of flows can be
an indicator of habitat availability. Timing of flows can serve as
a measure of flow requirements needed for species to complete
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TABLE 1 Maximum total length and mean catch weight statistics for each study fish species from the Dai fishery, 2002–2008.
Species names Photo of each species Max
total
length,
cm
Mean
daily
catch
weight,
kg
SD
daily
catch
weight,
kg
Mean
log
daily
catch
weight
SD log
daily
catch
weight
Economic value;
role in the
food-web
Pangasianodon
hypophthalmus (large river
catfish)
158.6 214.07 250.02 4.90 0.96 High value; predator/
omnivore
Cirrhinus microlepis
(medium-large carp)
79.3 108.11 125.14 4.27 0.83 High value; predator/
omnivore
Cyclocheilichthys enoplos
(medium-large carp)
90.3 61.53 98.48 3.09 1.44 High value; predator/
omnivore
Osteochilus melanopleurus
(medium-large carp)
73.2 270.29 283.39 4.87 1.44 High value; herbivore
Henicorhynchus entmema
(small mud carp)
18.3 11.39 4.55 2.35 0.41 Low value;
prey/omnivore/keystone
Labiobarbus leptocheilus
(small mud carp)
15.5 8.96 3.63 2.11 0.41 Low value;
prey/omnivore
SD is standard deviation. Max total length: is “the length from the tip of snout to the tip of depressed caudal fin” measured from the fish catch. Mean log daily catch weight was computed by
log-transforming individual catches and then taking the mean of these log-transformed catches.
parts of the life cycle or as an indicator of stress (e.g., floods
or droughts). The rate of change of flows can illustrate changing
habitat conditions (Richter et al., 1996). Our flow characteristics
were calculated using Tonle Sap River discharge (Tonle Sap Lake
outflow) from October to March with equations from Kummu et al.
(2014):
F=(WLPK )1.2 ∗(|WLPP −WLKL|)0.5 (1)
QTSR,in = −15.0467 ∗F2+859.839 ∗F−782.264 (2)
QTSR,out =8.784 ∗F2+434.465 ∗F+167.151 (3)
where F is the seasonal flow direction from the Mekong River
toward Tonle Sap Lake or from Tonle Sap Lake toward the Mekong
River, WLPK is water level (m) at Prek Kdam on the Tonle Sap
River, WLPP is the water level (m) at Phnom Penh Port at the
confluence of the Tonle Sap River and Mekong River, WLKL is the
water level (m) at Kompong Luong in Tonle Sap Lake, QTSR,in
is Tonle Sap River flow (m3s−1) into Tonle Sap Lake during the
wet season, and QTSR,out is the Tonle Sap River flow (m3s−1)
out of Tonle Sap Lake during the dry season. Daily Tonle Sap
River flows were calculated for 2002 to 2008. The hydrograph for
Tonle Sap River shows the dry season as negative flows leaving
Tonle Sap Lake to the Mekong River and the wet season as
positive flows toward Tonle Sap Lake (Supplementary Figure 8).
Precipitation data at the Kampong Chhnang gauge station was
obtained from the Mekong River Commission (2020). In each
water year, the daily data of each environmental predictor
corresponding to fish CPUE data (i.e., between October and
March from each water year) were combined and used as
model input.
2.3 Modeling and statistical analysis
To identify hydrologic and environmental predictors of
fish migration, we used Mixed Effects Random Forest (MERF;
Capitaine et al., 2021). MERF is a new approach to model
hierarchical data with random forests (Pellagatti et al., 2021) and
adapted to model longitudinal data by adding a stochastic process
for covariance structure or serial correlations between predictors
and outcomes over time (Capitaine et al., 2021). This random
forest-based approach has previously been used to predict the
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TABLE 2 Description of 23 predictor variables and their biological signal for fish.
Characteristic Code Predictor description Mean Min–max Biological signal
Flow FL1 Cumulative flow by Julian day (m3s−1) 357,103 4,371–711,691 Movement/growth
Flow FL2 Difference between current year Julian day cumulative flow
and average Julian day cumulative flow (m3s−1)
−7,926 −401,914–216,825 Movement/growth
Flow FL3 Tonle Sap River flow calculated with Equations (1–3) (m3s−1) 3,535 292–9,774 Movement/growth
Magnitude MF1 Number of days in a water year that flow exceeded the 75th
percentile flow (count)
35.4 0–46 Habitat availability
Magnitude MF2 Number of days in a water year that flow was below the 25th
percentile flow (count)
1.9 0–29 Habitat availability
Magnitude MF3 Flow magnitude exceeds 75th percentile of flow in each water
year (binary)
0.3 0–1 Habitat availability
Magnitude MF4 Flow magnitude is below 25th percentile of flow in each water
year (binary)
0.1 0–1 Habitat availability
Magnitude MF5 Water level at Prek Kdam (m) (Supplementary Figure 9) 4.5 1.0–9.4 Habitat availability
Magnitude MF6 Water level at Phnom Penh Port (m) 4.0 1.1–8.9 Habitat availability
Magnitude MF7 Water level at Kompong Luong (m) 5.6 1.2–9.6 Habitat availability
Timing TF1 Days since annual 1-day minimum (count) −21.4 −169–144 Life cycle completion/stress
Timing TF2 Days since annual 1-day maximum (count) 56.5 −25–161 Life cycle completion/stress
Timing TF3 Days since annual 3-day minimum (count) −79.3 −168–71 Life cycle completion/stress
Timing TF4 Days since annual 3-day maximum (count) 65.1 −25–161 Life cycle completion/stress
Timing TF5 Days since annual 7-day minimum (count) −82.5 −170–70 Life cycle completion/stress
Timing TF6 Days since annual 7-day maximum (count) 50.6 −26–138 Life cycle completion/stress
Timing TF7 Days since annual 30-day minimum (count) −81.2 −170–86 Life cycle completion/stress
Timing TF8 Days since annual 30-day maximum (count) 39.5 −59–135 Life cycle completion/stress
Timing TF9 Days since annual 90-day minimum (count) −81.2 −170–86 Life cycle completion/stress
Timing TF10 Days since annual 90-day maximum (count) 28.8 −47–118 Life cycle completion/stress
Rate of change RC Change from previous day (m3s−1)−25.8 −2,030–1,944 Habitat change
Precipitation OP1 Daily precipitation at Tonle Sap River at Kampong Chhang
gauge station (mm) between October and March
(Supplementary Figure 10)
0.8 0–98.5 Life cycle completion/
movement
Lunar cycle OP2 Daily lunar cycle (0: no moon, 1: full moon) 0.5 0.04–0.96 Movement
The numerical data of each variable (e.g., cumulative flow, counts, and water level measurement) was calculated for each water year.
invasion success of a freshwater fish species in diminished riparian
systems of temperate regions of North America (Krabbenhoft and
Kashian, 2022). The robustness of the approach has also been
reported in several medical and public health studies (Capitaine
et al., 2021;Haran et al., 2021).
MERF was developed based on a generalized linear mixed-
effects model, which estimates fixed effects by using a random forest
algorithm that constructs multiple ensemble trees. When the most
important predictors are identified in the most trees, their input
values are then averaged across all trees to regress against total
catch weight of each fish species. MERF has fixed (or population-
averaged) effects and random effects. This approach allows for
multiple covariates and non-linear effects (Pellagatti et al., 2021).
A MERF model was implemented for each species. Log-catch
weight of each species was the response variable and hydrologic and
environmental conditions were the predictors. Environmental data
are from six October—March dry seasons, spanning 2002–2008.
All predictors were represented as fixed effects, except water levels
in Phnom Penh and Kompong Luong (MF6 and MF7), which
were modeled as random effects because they are influenced by
the Mekong River and Tonle Sap Lake water level, respectively.
The mtry parameter in the model controls the number of candidate
variables to select at each split of a tree. We set mtry to 7, following
the default setting where mtry equals the number of predictors
divided by 3, exceeding the suggested minimum mtry value of 5
(Hastie et al., 2008). For modeling log-catch weight of each fish
species, 15 replicates were made to assure model stability. The 15
replicates provide 15 samples to estimate the distribution of log-
catch weight of each species (Minitab, 2017). The MERF model was
performed using the function “MERF” of the LongituRF package of
R (Capitaine et al., 2021).
The predicted fish log-catch weight obtained from the fitted
MERF was recorded and then correlated against the observed
log-catch weight to determine model performance using the
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coefficient of determination (R2). Predictor variable importance
was assessed using the percentage increase in mean square
error (%IncMSE) and the percentage increase in node purity
(%IncNodePurity) of regression trees to identify the most
important predictors. The percentage increase in node purity is
measured by the residual sum of squares and defines the level
of homogeneity averaged over all trees. Higher values of the two
measures indicate more important predictor variables (Dewi and
Chen, 2019). Then the mean of R2, percentage increase in mean
square error, and percentage increase in node purity were calculated
across the 15 replicates for final model performance and ranked
variable importance. The variables with the highest average mean
square error and largest percentage increase in node purity were
considered the most important predictors of fish catch weight for
each fish species. This correlation analysis was conducted using the
function “cor” of the base stats package of R (R Core Team, 2024).
To test whether a group of fish species was affected by
similar factors, cluster analysis was further conducted based
on dissimilarities between the daily catch of each fish species,
using the “correlation” method in the “pvclust” function of the
pvclust package in R (Suzuki and Shimodaira, 2006). A bootstrap
probability (BP) value ranging from 0 to 100%, indicating the
lowest to highest level of correct clustering, was calculated based
on the multiscale bootstrap resampling procedure (nboot =1,000
for this study) for each cluster (Suzuki and Shimodaira, 2006). All
statistical modeling and analyses were performed using R statistical
programing language (R Core Team, 2024).
3 Results
MERF performed well in predicting the log-catch weight of
each fish species, with models ranging from R2=0.89 to 0.93 and
standard deviation ranging from 0.001 to 0.01 (Figure 2). When
predicting the large striped catfish, P. hypophthalmus, the difference
between current year Julian day cumulative flow and average Julian
FIGURE 2
Mixed Eects Random Forest (MERF) performance based on model goodness of fit (R2±standard deviation) between the observed and the
predicted log-catch weight of each fish species.
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FIGURE 3
Mixed Eects Random Forest (MERF) importance ranking of hydrologic and environmental predictors based on contribution percentage of each
variable measured by the percentage increase in mean square error and the percentage increase in node purity. Error bars show 1 standard deviation
from the mean. See Table 2 for predictor definitions. (A) Pangasianodon hypophthalmus.(B) Cirrhinus microlepis.(C) Cyclocheilichthys enoplos.(D)
Osteochilus melanopleurus.(E) Henicorhynchus entmema.(F) Labiobarbus leptocheilus.
day cumulative flow (FL2) was the most important predictor,
measured by both the percentage increase in mean square error
and the percentage increase in node purity. Cumulative flow (FL1)
and days since annual 3-day minimum (TF3) alternated between
the second and third most important variables, followed by the
water level at Prek Kdam (MF5) or daily moon illumination (OP2;
Figure 3A).
For the medium-size cyprinid species, C. microlepis, the most
important predictors were also the difference between current year
Julian day cumulative flow and average Julian day cumulative flow
(FL2) and cumulative flow (FL1). Tonle Sap River streamflow (FL3)
and water level at Prek Kdam (MF5) alternated between the third
and fourth most important variables (Figure 3B). For the other two
medium-size cyprinid species, C. enoplos and O. melanopleurus,
the number of low-flow days (MF2) was the most important
predictor, followed by the difference between current year Julian
day cumulative flow and average Julian day cumulative flow (FL2),
water level at Prek Kdam (MF5), cumulative flow (FL1), and days
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FIGURE 4
Cluster analysis of the six fish species in the Lower Mekong Basin based on the CPUE computed from 2002 to 2008. Numbers in the parentheses are
the bootstrap probability value (%) indicating the level of correct clustering. Height is the distance at which the CPUE of fish species are fused.
since annual 1-day minimum (TF1) for C. enoplos (Figure 3C),
and days since annual 1-day minimum (TF1), days since annual
3-day minimum (FL3), the difference between current year Julian
day cumulative flow and average Julian day cumulative flow (FL2),
water level at Prek Kdam (MF5) and/or lunar cycle (OP2) and rate
of change (RC) for O. melanopleurus (Figure 3D).
For the smaller cyprinid species H. entmema, the most
important predictors were cumulative flow (FL1) and days since
the annual 30-day-maximum flow (TF8), followed by water level at
Prek Kdam (MF5), lunar cycle (OP2) and the difference between
current year Julian day cumulative flow and average Julian day
cumulative flow (FL2; Figure 3E). For L. leptocheilus, the most
important predictors were days since the 7-, 30-, or 90-day-
minimum flow (TF5, TF7, and TF9), followed by the water level
at Prek Kdam (MF5), and days since the annual 7-day-maximum
flow (TF6; Figure 3F).
Based on the cluster analysis, three clusters were identified:
cluster Ia grouped P. hypopthalmus and C. microlepis, cluster Ib
grouped O. melanopleurus and C. enoplos, and cluster II grouped L.
leptocheilus and H. entmema. Clusters Ia, Ib, and II have bootstrap
probability values of 44, 44, and 100%, respectively (Figure 4).
These values indicate the percentage of correct clustering out the
1,000 bootstraps.
4 Discussion
Multiple flow metrics, including streamflow, flow magnitude,
and flow timing were key factors cueing migration of the six species
from Tonle Sap Lake to the Mekong Basin and upstream tributaries.
Streamflow is an important driver of fish migration in other systems
(Rytwinski et al., 2020), including catfish and other mega fish in
tropical Australia (O’Mara et al., 2021) and salmonids in temperate
river systems (Morales-Marín et al., 2019;Goodrum and Null,
2022). Altogether, flow characteristics are critical for migratory
fish—they predicate habitat, food availability, reproduction success,
and movement timing (Ngor et al., 2018b;Chan et al., 2019;
Chhuoy et al., 2022). However, we did not find that lunar cycle or
precipitation were correlated with fish migrations, although these
predictors have been previously identified in the Lower Mekong
River (Baird and Flaherty, 2001;Baran, 2006).
Streamflow metrics (FL predictors) were key drivers of large
and medium fish species migration in cluster Ia, comprising P.
hypophthalmus and C. microlepis. These results add support to
previous research showing that streamflow is an important driver
of fish migrations (Poff, 1997;Richter et al., 1997). For example,
P. hypophthalmus migrate through the Tonle Sap River during low
streamflow, which corresponds to the dry period between March
and April, then spend the wet season in their spawning habitat in
the Mekong River (Chhuoy et al., 2022). Streamflow also drives C.
microlepis larvae dispersal (Chhuoy et al., 2022).
For the medium-size fish C. enoplos and C. microlepis in cluster
Ib, flow magnitude metrics (MF predictors), especially low-flow
days (MF2), were the most important factors influencing migration.
Migration of both C. enoplos and O. melanopleurus was triggered
by the occurrence of low-flow days (MF2), consistent with previous
research showing these species migrate to the Mekong River and
upstream tributaries to reproduce when low flows are between
1,000 and 5,000 m3s−1(Baran, 2006).
For the small mud carps in cluster II, flow volume and timing
were particularly important for movement. Flow timing, as days
since the 30-day flow maximum (TF8) was an important predictor
for H. entmema. This coincides with a previous study that showed
that migration of H. entmema occurs at the Dai fisheries about 15
weeks after peak water level in the Tonle Sap River at Prek Kdam
(Chan et al., 2019). The L. leptocheilus migration is sensitive to flow
timing, in particular the period following the 7-day minimum flow
(TF5) and the 30- and 90-day minimum flow (TF7 and TF9). All
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Sor et al. 10.3389/wsc.2024.1426350
of these are indicators that timing surrounding the critically low
flow period is a main driver for small mud carp species migration,
consistent with previous studies (Chan et al., 1999;Rytwinski et al.,
2020;O’Mara et al., 2021).
Migratory species primarily migrate from Tonle Sap Lake to
the Mekong River during the recession limb of the Tonle Sap
River and Mekong River, typically from October to February (Ngor
et al., 2018a;Chan et al., 2019). As indiscriminate fishing alters fish
abundance and biodiversity, shifting catch from larger to smaller
species (Ngor et al., 2018c), our research suggests the hydrologic
and environmental conditions when fishing could be reduced
to protect larger species. Limiting fish catch could be based on
streamflow volume (e.g., FL1, FL2), flow magnitude (MF2, MF5),
and flow timing (TF1, TF3). The flow variables identified here
provide thresholds upon which to base decisions. For example, a
moratorium on fishing to preserve species could be enacted when
water recedes at Tonle Sap Lake.
One of the limitations of our study and dataset is that it does
not include water quality and other abiotic metrics, which have
previously been shown to influence fish habitat and ecosystem
function (Olden and Naiman, 2010). Water quality variables such
as turbidity can affect fish gill function and increase or decrease fish
movement (Hildebrandt and Parsons, 2016). Water temperature
influences metabolic rates, physiology, and reproduction of fish,
which affects fish movement and growth (Webb et al., 2008).
Nutrients are key fish migration triggers because when food is
scarce, fish compete for food and migrate in search of better feeding
grounds (Baran, 2006).
Another limitation is the challenge of interpreting the MERF
model. Although this approach has been implemented in several
fields like freshwater ecology (Krabbenhoft and Kashian, 2022),
education sciences (Pellagatti et al., 2021), and public health
(Haran et al., 2021), improved documentation and better parameter
guidelines would improve model utility. In particular, the model
is sensitive to mtry value, where small or large mtry leads to
underfitting or overfitting the model, respectively (Hastie et al.,
2008). Functional explanation of the model results, such as
linear or non-linear, positive or negative relationships should be
further investigated.
5 Implications for the future
Flow characteristics have changed remarkably in the twenty-
first century due to anthropogenic disturbances like hydropower
dams, land use changes, and drought (Ngor et al., 2018b;Null
et al., 2021;Chann et al., 2022;Sor et al., 2023). Future work
aiming to identify main drivers of fish migration would benefit
from combining every possible category of predictors, such as
hydrological variables (as in the case of our study), and other
physical-chemical variable such as water temperature, turbidity,
dissolved oxygen, phosphate, and nitrate as these variables have
been reported to influence fish communities in the Mekong Basin
(Chea et al., 2016).
The Dai fishery in the Tonle Sap system contributes 60% to
the annual commercial fish market in Cambodia and feeds tens
of millions of people (McCann et al., 2015;Yoshida et al., 2020).
However, the Dai fishery is dependent on the unique hydrology
of the Tonle Sap system that supports migratory fish. Previous
research on the impacts of dams throughout the Mekong River
Basin have provided evidence of the alteration of flows and
fragmented migratory fish habitats (Arias et al., 2014;Hecht et al.,
2019;Sor et al., 2023). With 11 proposed dams on the mainstem
Mekong River, and more in tributaries, flow alteration could
shift or impact migratory cues for fishes, potentially disrupting
migratory patterns. Our findings of the key drivers of fish migration
for six species provide a better understanding of hydrologic and
environmental conditions needed to maintain fish migrations, fish
harvest, and biodiversity in the Lower Mekong Basin and Tonle
Sap system.
Data availability statement
The analyzed data in this study is provided by the Mekong
River Commission and is available in raw form via request to the
Mekong River Commission. Requests to access the datasets should
be directed to PN (pengbun.ngor@gmail.com).
Ethics statement
The animal study was approved by the Inland Fisheries
Research and Development Institute (IFReDI), Cambodia Fisheries
Administration, and all methods were carried out in accordance
with relevant guidelines and regulations of IFReDI. The study
was conducted in accordance with the local legislation and
institutional requirements.
Author contributions
RS: Writing – review & editing, Writing – original draft,
Visualization, Validation, Software, Project administration,
Methodology, Investigation, Formal analysis, Data curation,
Conceptualization. LP: Writing – review & editing, Validation,
Data curation, Conceptualization. ZH: Writing – review & editing,
Supervision, Project administration, Investigation, Funding
acquisition. SC: Writing – review & editing, Supervision, Project
administration, Funding acquisition. PN: Writing – review &
editing, Project administration, Investigation, Data curation. SN:
Writing – review & editing, Validation, Supervision, Resources,
Project administration, Methodology, Investigation, Funding
acquisition, Conceptualization.
Funding
The author(s) declare financial support was received for
the research, authorship, and/or publication of this article. This
research was funded by United States Agency for International
Development’s “Wonders of the Mekong” Cooperative Agreement
No: AID-OAA-A-16-00057.
Frontiers in Freshwater Science 09 frontiersin.org

Sor et al. 10.3389/wsc.2024.1426350
Acknowledgments
We thank the Mekong River Commission for providing the
environmental database for our analysis.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
The author(s) declared that they were an editorial
board member of Frontiers, at the time of submission.
This had no impact on the peer review process and the
final decision.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/ffwsc.2024.
1426350/full#supplementary-material
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