# Impact of seasonal hydrological variation on tropical fish assemblages: abrupt shift following an extreme flood event

Ratha Chea, Thomas K. Pool, Mathieu Chevalier, Pengbun Ngor, Nam So, Kirk O. Winemiller, Sovan Lek, Gaêl Grenouillet

► To cite this version:

Ratha Chea, Thomas K. Pool, Mathieu Chevalier, Pengbun Ngor, Nam So, et al.. Impact of seasonal hydrological variation on tropical fish assemblages: abrupt shift following an extreme flood event. Ecosphere, 2020, 11 (12), pp.e03303. 10.1002/ecs2.3303. hal-04717165

HAL Id: hal-04717165

https://cnrs.hal.science/hal-04717165v1

Submitted on 1 Oct 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Impact of seasonal hydrological variation on tropical fish assemblages: abrupt shift following an extreme flood event

RATHA CHEA ID,<fnref n="1" />,<fnref n="2" />,† THOMAS K. POOL,<fnref n="3" /> MATHIEU CHEVALIER ID,<fnref n="2" /> PENGBUN NGOR ID,<fnref n="4" /> NAM SO,<fnref n="4" />,<fnref n="5" />
KIRK O. WINEMILLER,<fnref n="6" /> SOVAN LEK,<fnref n="2" /> AND GAËL GRENOUILLET<fnref n="2" />,<fnref n="7" />

**Citation:** Chea, R., T. K. Pool, M. Chevalier, P. Ngor, N. So, K. O. Winemiller, S., Lek, and G. Grenouillet. 2020. Impact of seasonal hydrological variation on tropical fish assemblages: abrupt shift following an extreme flood event. Ecosphere 11(12):e03303. 10.1002/ecs2.3303

**Abstract.** Pulsing hydrology is a major factor affecting fish communities in tropical river-floodplain ecosystems. Species responses to flow alteration are however poorly understood and are thus difficult to predict. Here, we investigated temporal changes in fish community structure (taxonomic and functional) in the Tonle Sap Lake, a floodplain ecosystem in Cambodia that supports the world largest inland fisheries and a UNESCO biosphere reserve. Fish catches were monitored daily at five locations around the lake between 2012 and 2015, with 53 species obtained over the course of the field study. Nine fish traits were recorded and used to characterize the functional composition of fish communities. Temporal changes in functional community structure in response to hydrological variation were assessed using multiple co-inertia analysis (MCOA) and cross-correlation function (CCF) approaches. Analyses revealed an abrupt shift in functional structure following extreme flood events in 2013 that included a marked decrease in large-bodied fish at high trophic levels. Also observed were seasonal changes in the relative abundance of life-history strategies, feeding habits, and accessory respiratory adaptations. Black fish (species that are relatively sedentary with an equilibrium-type life-history strategy) dominated during low-water periods, whereas white fish (migratory species with opportunistic or periodic life-history strategies) were dominant during high-water periods. Dynamics of local assemblage structure were fairly synchronous spatially and were independent of the distance between locations. Species responses to the peak flood pulse were estimated to lag 1–2 months. Findings from this study indicate that management of fisheries resources in the Tonle Sap Lake should consider differential responses of species to hydrology as a function of life-history strategies and other functional traits as well as factors that may influence time lags in population and community changes to extreme flow events.

**Key words:** fish community; flood pulse system; functional traits; hydrological variation; spatial synchrony; Tonle Sap Lake.

**Received** 27 April 2020; revised 1 August 2020; accepted 14 August 2020. Corresponding Editor: Michael T. Bogan.

**Copyright:** © 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution
License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

† E-mail: chearatha@ubb.edu.kh

<footnote n="1">¹Laboratory of Freshwater Ecology, Graduate School, University of Battambang, Battambang, Cambodia</footnote>

<footnote n="2">²Laboratoire Evolution & Diversité Biologique (EDB UMR5174), Université de Toulouse, CNRS, IRD, UPS, Toulouse Cedex, France</footnote>

<footnote n="3">³School of Aquatic and Fishery Sciences (SAFS), University of Washington, Seattle, Washington 98195 USA</footnote>

<footnote n="4">⁴Fisheries Administration, Inland Fisheries Research and Development Institute (IFREDI), Phnom Penh, Cambodia</footnote>

<footnote n="5">⁵Mekong River Commission Secretariat (MRCS), Vientiane, Lao PDR</footnote>

<footnote n="6">⁶Department Wildlife and Fisheries Sciences, Texas A&M University, College Station, Texas 77843 USA</footnote>

<footnote n="7">⁷Institut Universitaire de France, Paris, France</footnote>
INTRODUCTION

Despite extensive research on plant and animal
community structure and ecosystem functioning
during the last few decades, our understanding
of relationships between community taxonomic
structure, species functional traits and ecological
dynamics and processes remains limited (Wine-
miller et al. 2015). This understanding is essential
for predicting population and community
responses to multiple stressors impacting biodi-
versity and ecosystems globally (Olden et al.
2010). For most ecosystems, our knowledge of
specific factors driving temporal and spatial vari-
ation in community structure of multiple fish
species and large rivers is too limited to set up
effective management strategies, and many have
called for research that addresses this fundamen-
tal issue (Carmel et al. 2013, Sutherland et al.
2013).

Research on the drivers of community dynam-
ics is especially critical for fisheries management,
particularly within tropical regions where spe-
cies abundance and diversity is high and fishing
pressure often is intense. In Southeast Asia, the
nutritional and economic demands placed on
natural ecosystems are greatly challenging biodi-
versity conservation and environmental sustain-
ability (Dudgeon 2011). Inland capture fisheries
play a crucial role in regional food security, pro-
viding nearly 60% of the animal protein for
human consumption, especially in developing
countries such as Cambodia, Lao PDR, Vietnam
(Hortle 2007, So et al. 2015). Accordingly, many
Asian rivers are now facing increasing environ-
mental degradation and pressures, that is, water
pollution, eutrophication, habitat fragmentation,
deforestation, which are adversely affecting bio-
diversity and ecosystem functions (Vörösmarty
et al. 2010, Dugan et al. 2010, Ziv et al. 2012,
Winemiller et al. 2016). Improved strategies for
fisheries management and biodiversity conserva-
tion are needed to mitigate impacts and sustain
ecosystem services. This will require understand-
ing of how specific hydrological components
influence spatial and temporal variation in fish
community structure (Olden et al. 2010, Pool
et al. 2010). Long-term monitoring of fish com-
munities in rivers has provided an effective
means to study compositional shifts; however,
few such efforts have been made in rivers of

Southeast Asia (Dudgeon 2003). The Mekong
River and other large biodiverse rivers in South-
east Asia have extensive floodplains and wet-
lands that provide invaluable ecosystem services
that support both biodiversity and human wel-
fare. Tropical river-floodplain ecosystems have
diverse fish communities that often support
important commercial and subsistence fisheries.
Fish production is enhanced by seasonal hydro-
logical pulsing that connects and expands flood-
plain lakes (Junk et al. 1989, Junk and Wantzen
2004). In Cambodia, the Tonle Sap Lake (TSL)
lies in a huge floodplain that is connected to the
Mekong River via the Tonle Sap River. The lake
volume changes dramatically between dry and
wet seasons, from 2500 to 15,000 km², depending
on the extent of flooding from the Mekong River
(60%) and in the surrounding area (30%).

The river flood pulse concept (FPC), an influ-
ential conceptual framework describing flood
pulse river dynamics, asserts that ecological
dynamics and productivity of river-floodplain
ecosystems are regulated by large seasonal
changes in flows that affect lateral connections
between aquatic and terrestrial landscapes (Junk
et al. 1989, Junk and Wantzen 2004). This frame-
work focuses on the lateral dimension of rivers
and contrasts with the river continuum concept
(RCC; Vannote et al. 1980) that asserts that local
nutrient dynamics and productivity and nutri-
ents are strongly dependent on the position
within the longitudinal fluvial gradient and are
influenced by upstream processes. The current
consensus in river ecology emphasizes the
hydrological regime as the principal driver of
ecological dynamics (Simões et al. 2013, Humph-
ries et al. 2014). Studies conducted on river-flood-
plain systems in the Americas, Europe, and
Africa have documented variation in fish com-
munity taxonomic and functional structure in
relation to hydrology and associated environ-
mental conditions (Winemiller 1989, Merigoux
and Ponton 1999, Merigoux et al. 2001, Tedesco
et al. 2008, Pease et al. 2012). In the Pantanal wet-
lands of South America, the average trophic posi-
tion of fishes declined during the transition from
the dry season to the wet (flood) season (Want-
zen et al. 2002). Merigoux and Ponton (1999)
reported a positive correlation between the den-
sity of characiform fishes and flood level in river
floodplains in French Guiana. Moreover, fish
functional traits were strongly related to variation in aquatic habitats and flow regimes. Specifically, fish size at maturity, diameter of oocytes, parental care, and body height tended to decline with greater temporal variability in river hydrology (Merigoux and Ponton 1999, Merigoux et al. 2001). Functional traits thus help to mechanistically link temporal changes in river fish communities to environmental drivers. The present study investigates the extent to which the taxonomic and the functional structure of fish communities in the Tonle Sap Lake are explained by variation in hydrology and associated environmental conditions in this flood pulse system.

Whereas recent research on Tonle Sap Lake fishes has demonstrated changes in biomass or species richness related to the annual flood pulse (e.g., Ngor et al. 2018), here we focus on seasonal changes in both taxonomic and functional composition of fish communities. We examined the temporal dynamics of fish assemblages across multiple locations within the lake and their responses to the lake's hydrological regime. We hypothesized that both taxonomic and functional changes in fish community would be strongly driven by the annual flood pulse and that the spatial synchrony in fish assemblage composition would not be related to the geographical distance between sites owing to the strong synchronizing effect of hydrological changes impacting all location similarly (Liebhold et al. 2004).

## MATERIALS AND METHODS

### Study system: Tonle Sap Lake

The Tonle Sap Lake (TSL, Fig. 1) is the largest and most productive freshwater lake in Southeast Asia and well known for its extraordinary seasonal flood pulse, fish diversity, and productive fisheries. During the wet (monsoon) season (May–October), the lake receives water from tributary rivers surrounding the lake area and the Mekong River via the Tonle Sap River, which during this period reverses its flow as it receives flood waters from the Mekong River. Water levels rise on average from 1 to 9 m, causing a huge expansion of the lake (~500–600%) into surrounding floodplains. When the TSL discharges water to the Mekong River during the dry season, this additional water volume mitigates sea water intrusion

in the Mekong Delta (MRC 2010). TSL drains 8065 km², ~11.3% of the total drainage area of the Mekong River Basin (MRB) and is characterized by annual rainfall ranges of 1300–1900 mm with an average temperature of 29°C (max: 33°C, min: 25°C). The hydrology of the lake is strongly dependent on the Mekong River flow regime with more than 60% of TSL's water flowing in from the Mekong, supplemented by river runoff from 12 sub-basins around the lake such as the substantial Stung Sen, Sangker, Pursat, Chinet river tributaries (Fig. 1). These sub-basins are nested within 5 provinces (Battambang [BB], Siem Reap [SR], Pursat [PS], Kampong Thom [KT], Kampong Chhnang [KC]; Fig. 1) around the lake, each containing floodplains that are mainly covered by shrubland, flooded forest, grass land, rice and lotus fields, which are the key habitats for many plant and animal species (Lamberts 2001, MRC 2010). The TSL supports more than 300 fish species and is an exceptionally productive ecosystem compared to other large lakes around the world (WorldFish 2007). The lake supports the world's largest inland fisheries, with harvests between 289,000 and 431,000 tons annually, representing more than 60% of Cambodian inland capture fisheries. Given these features, the TSL provides good model system to investigate community dynamics in a strongly pulsing environment.

### Fish data

Fish data used in this study were collected during the “Fish abundance and diversity monitoring in the Tonle Sap Lake” project associated with the Mekong River Commission’s (MRC) Fisheries programs (Ngor 2012). The monitoring program included five locations in five provinces surrounding the lake, that is, Kampong Chhnang, Pursat, Battambang, Siem Reap, and Kampong Thom (Fig. 1), from January 2012 to October 2015. At each location, three fishermen recorded their daily catches, species composition, individual abundance, as well as the type and dimension of fishing gear they used. The catch monitoring methods were adapted from the MRC’s regional monitoring program on Fish abundance and diversity in the Lower Mekong Basin (FEVM 2007). Fishermen were trained to sample their typical daily catch, to identify the fish species, and to measure the length and weight of specimens. Each specimen was
<figure><img src="image_3.png" /><figcaption>Fig. 1. Tonle Sap Lake and geographic locations of fish monitoring sites (star).</figcaption></figure>

identified to the species level and validated with
the use of fish photo flipcharts featuring more
than 170 fish species found in the TSL. To ensure
the quality of monitoring data, all records were
independently reviewed by MRC scientists on a
quarterly basis.

Only fish data collected using gillnets (98% of
the fishing gears used) were selected for the
study with mesh sizes ranging from 2 to 6.5 cm
and an average dimension of 850 m in length by
1.5 m in depth. The catch was expressed in total
number of individual specimens collected per
species resulting in 209 species identified during
the monitoring program. Only the species pre-
sent >5% of the total observations, that is, 60 d
over the whole sampling period (1172 d), were
kept for analyses (n = 53 species). To minimize
the range and reduce skewness of the distribu-
tion, fish abundances from the three fishermen

were averaged at each site and ln(x + 1) – trans-
formed.

Hydrological data

Daily water levels were collected for the same
period (2012–2015) as fish data from the main
hydrological station of MRC located in Kampong
Loung (PS; Fig. 1). The water levels fluctuated
seasonally with the lowest level in April–May
and peak in September–October. Within the
monitoring period, the flood in September 2013
was exceptional with the average return period
(i.e., recurrence interval) of this hydrological
event estimated to be 12 yr (MRC 2015).

Functional traits

Nine functional traits (maximum size, mean
trophic level, habitat, life-history strategy, ecolog-
ical guild, feeding habitat, spawning habitat,
water column position, and accessory respiratory) were used to examine the association of community structure to functional traits. All traits were collected and updated from different sources (see Appendix S1: Table S1). Size, that is, the maximum total length of fish (cm) ever recorded for a species, trophic level, and habitat data were recorded from FishBase (Froese and Pauly 2019). The size and trophic level of fish ranged from 13 to 158 cm and from 2 to 4.5, respectively. Habitat associations were categorized into three types, that is, pelagic (PEL), benthopelagic (BEN), and demersal (DEM). The life-history strategies were based on the model developed by Winemiller and Rose (1992), which classify the life history of fish into three endpoint strategies, that is, equilibrium (EQU), periodic (PER), and opportunistic (OPP), based on the juvenile survivorship, fecundity, and the age of reproduction (Winemiller and Rose 1992, Winemiller 2005).

Each fish species was assigned to one of three migration guilds using data from the long-term bio-ecological study conducted by the Inland Fisheries Research and Development Institute (IFReDI) and the MRC (Poulsen et al. 2002, Baran et al. 2007, MRC 2018). White fishes perform annual long-distance migrations along the Mekong River mainstream or between the Tonle Sap Lake and Mekong River. Gray fishes migrate short distances, often between the Tonle Sap Lake, surrounding floodplains, tributaries, and/or Mekong River. Black fishes do not migrate and stay permanently in the same water body such as lakes, ponds, rivers, or paddy fields. Many black fishes possess accessory respiratory adaptations that allow them to tolerate water with low oxygen concentration.

Other traits such as spawning and feeding habits were collected from MRC Mekong Fish Database (2003) Poulsen et al. (2004). Spawning habitats were categorized into two types, that is, floodplains (FLP) and river channels (RV), and two categories were used for feeding habits, that is, littoral (LIT) and offshore and littoral (OFF&LIT). Last, the water column position and accessory respiratory traits were modified based on descriptions for tropical fishes from floodplain habitats in South America (Winemiller 1989). Species were classified as normally occupying a vertical position in the water column near the bottom-middle (B-M) or middle-surface (M-S), and as

having (YES) or not having (NO) an accessory respiratory organ, such as a suprabranchial chamber (e.g., climbing perch, Anabantidae; snakehead, Channidae; or walking catfish, Clariidae).

Biological information for Tonle Sap fishes is scarce. Therefore, species trait values and classifications relied on previous studies conducted in the Mekong Basin, particularly the ecological and fish migration studies conducted by Poulsen et al. (2002, 2004). Some classifications were inferred by KOW based on information from other regions of the Lower Mekong Basin (Ou and Winemiller 2016, Ou et al. 2017, Bower and Winemiller 2019a, b) and floodplain systems in the Neotropics (e.g., Winemiller 1989).

## Statistical analyses

Time series of fish data were analyzed using multiple co-inertia analysis (MCOA), which is a multi-table ordination method that can be used to assess ecological patterns in space and time based on the covariance optimization criterion between individual table ordinations and a reference ordination (Chessel and Hanafi 1996, Bady et al. 2004). Despite its ability to quantify spatiotemporal community patterns, MCOA is rarely used in ecological studies compared to other ordination methods, for example, principal component analysis (PCA), redundancy analysis (RDA), or canonical correspondence analysis (CCA). In our study, MCOA was the most appropriate ordination method because it allowed us to investigate changes in fish communities by simultaneously quantifying the temporal variations of fish assemblages of the individual tables (i.e., communities at each site) and by producing the reference ordination, which is the common structure shared by the individual tables. Thus, MCOA was performed on the 5 individual tables, that is, five monitoring sites (BB, SR, PS, KT, KC), and the reference structure can be viewed as the temporal pattern within the lake. Temporal changes in fish community composition were summarized by MCOA synthetic scores, which are the optimized covariance values from the individual tables. We used the coefficient RV, which measures the correlation between the temporal dynamics of the individual tables and of the temporal reference structure, to quantify the strength of the relationship between individual table and common structure (Robert
and Escoufier 1976). This coefficient ranges from 0 to 1 with low values indicating a poor synchrony between the temporal patterns of individual tables (i.e., locations) and the reference structure. The reference structure refers to the temporal change of fish community composition for the Tonle Sap lake as whole. It is the common structure shared by the five individual fish tables (our five sampling sites) based on the covariance optimization criteria from the MCOA procedure.

Furthermore, to estimate spatial synchrony in temporal variations of community structure across the five sites, we used a multivariate normal distribution: $Y_{ij} \sim MVN(\mu_j, \Sigma_{ij})$, where $Y_{ij}$ are the coordinates at site $j$ and time $i$ along a given MCOA axis, $\mu_j$ are the site-specific means and $\Sigma_{ij}$ is a variance-covariance matrix of the form:

$$ \Sigma_{ij} = \begin{pmatrix} \sigma_1^2 & \cdots & \rho_{15} \sigma_1 \sigma_5 \\ \vdots & \ddots & \vdots \\ \rho_{15} \sigma_1 \sigma_5 & \cdots & \sigma_5^2 \end{pmatrix} $$

where diagonal elements represent the variance within each site and off-diagonal elements represent the covariance between sites. $\rho$ is the temporal correlation coefficient between each site and represents the temporal synchrony in community structure (Santin-Janin et al. 2014). For two sites, $\rho$ was calculated as

$$ \rho_{jj'} = \frac{\sigma_j \sigma_{j'}}{\sqrt{\sigma_j^2 \sigma_{j'}^2}} $$

where $j$ stands for one site and $j'$ for another site. To obtain the posterior distribution of $\rho$ between each site combination, we adopted a Bayesian approach in combination with the Monte Carlo Markov Chain (Clark and Bjørnstad 2004). To ensure that the variance-covariance matrix ($\Sigma$) was positive definite, we used an inverse Wishart distribution, whereas diffuse priors (i.e., large uniform distributions) were used for the parameter $\mu_j$. For each MCOA axis, we generated three chains with length 30,000 and discarded the first 20,000 as burn-in. For each chain, we chose initial values in different regions of parameter space. Convergence was visually assessed and confirmed using the Gelman and Rubin statistic (Gelman and Rubin 1992).

To examine the relationships between temporal changes in community structure and functional traits, the quantitative traits were regressed against MCOA coordinates using linear models, while the effect of qualitative traits was tested using non-parametric Kruskal-Wallis tests. All the relationships were tested for the first three axes of MCOA, and p-values were adjusted for multiple hypothesis testing using a Benjamini-Hochberg procedure (i.e., sequential modified Bonferroni correction). The tests of qualitative traits were performed only for the categories containing enough species. As a result, only PER and EQU were tested for life-history strategies, while OPP was excluded ($n = 2$). Finally, we assessed the changes in taxonomic structure by testing differences in the MCOA scores of the species from the five main fish families in the lake (i.e., Cyprinidae, Bagridae, Pan-gasidae, Siluridae, and Osphronemidae).

## Linking community changes to hydrological regimes

To assess the link between temporal changes in the fish community and the hydrological regime of the lake, we used cross-correlation functions (CCF). CCF is used to quantify the relationship between two time series ($x_t$ and $y_t$) as a function of time lag ($h$) between one to another, that is, the correlation between series $x_{t+h}$ and $y_t$ for $h = 0, \pm 1, \pm 2$ and so on (Venables and Ripley 2002). In our case, $x_t$ and $y_t$ were, respectively, the synthetic scores of MCOA and water level. The time lags ($h$, days), indicating the response of fish community to the hydrological changes, were estimated from the maximum value of CCF coefficients. Specifically, when the time lag $h$ is negative ($-h$), it displayed the correlation between MCOA score at a time before $t$ and water level at time $t$; in other words, MCOA score leads water level. On the other hand, MCOA score lags water level when the time lag $h$ is positive (Shumway and Stoffer 2015).

All data analyses were conducted in R.3.6.1 using ade4 and R2jags packages (R Core Team 2019).

## RESULTS

### Temporal changes in fish community structure

The 53 fish species in the dataset represented 40 genera, 16 families, and 7 orders. Cypriniformes,
Siluriformes, and Perciformes represented 90% of the total species, and the remaining orders (i.e., Clupeiformes, Beloniformes, Osteoglossiformes, Synbranchiformes) accounted for 10% (Appendix S1: Table S1). Cyprinidae was the most species-rich family (45% of species), followed by Bagridae (13%), Siluridae (8%), Pangasiidae (6%), and Osphronemidae (6%), with the remaining 11 families representing less than 20% of the species.

The temporal changes in fish community structure exhibited spatial synchrony across the five sites, with mean temporal correlation ρ ranging from 0.43 to 0.66 and no significant relationships between the strength of synchrony and the geographical distance between sites. Temporal changes in the TSL fish community were summarized by the reference pattern on the first three axes of MCOA, which explained 53.24% of the total variance from the fish abundance data sets (25.28% on F1 axis, 17.09% on F2 axis, and 10.87% on F3 axis; Fig. 2). The values of RV coefficients (a multivariate generalization of the correlation coefficient between two sets of variables) ranged from 0.52 (BB) to 0.75 (SR). On F1, the synthetic scores increased significantly over the study period from negative values to positive ones, revealing a marked shift in community composition after the wet season in 2013, while on F2 and F3, seasonal changes related to the flood pulse cycle were apparent (Fig. 2).

Six of the nine functional traits were strongly associated to the temporal changes in the fish community, with significant relationships detected on the first and third of MCOA axes. Indeed, F1 was characterized by a shift of size classes and trophic levels linked to the extreme flood event in 2013, with greater abundance of large-bodied fish with high trophic levels before the flood (Fig. 3). In addition, seasonal patterns in community structure apparent on F3 were associated with life-history strategy and movement guilds (Fig. 3, Appendix S1: Table S1 and Appendix S2: Table S1). Specifically, the community tended to be dominated by black fishes with an equilibrium-type life-history strategy during the dry season when water levels were low. When water level increased during the wet season, the community became dominated by gray and white fishes with periodic and opportunistic life-history strategies. Feeding habits and

accessory respiratory traits were associated with seasonal hydrology as well, with higher abundance of species feeding in littoral areas and possessing accessory respiratory adaptations during the dry season, and more offshore feeding species that lack accessory respiratory adaptations during the wet season. Nevertheless, no significant relationships were observed on F2. Habitat, spawning habitat, and water column position were not related to temporal changes in community structure.

*Linking the temporal changes in fish community to hydrological regime*

The CCF revealed a significant relationship between the temporal changes in functional community structure and hydrological regime (Appendix S3: Fig. S1). The response of fish community summarized by the first two axes of MCOA lagged behind the hydrological regime, with time lags ranging between -38 and -58 d for F1, -50 and -60 d for F2, and +20 and +30 d for F3. Cross-correlations were lowest for F1 ($r^2$ = 0.40) and highest for F3 ($r^2$ = 0.75). These responses revealed that fish movements into and out of the lake occurred about one to two months before (F1 and F2) or after (F3) the peak of the annual flood pulse.

DISCUSSION

*Temporal changes in fish community structure*

Fish communities in the Tonle Sap Lake underwent a major shift in size structure and distribution of species trophic positions in association with hydrological variation. During the low-water period, fish communities were dominated by medium-large fishes at relatively high trophic positions. In contrast, during the flood pulse, the relative abundance of large/medium-sized cyprinids and catfishes and several small cyprinids and gouramis (Osphronemidae), including species at relatively low trophic positions, increased rapidly. This shift was more abrupt following the extreme flood pulse in 2013 produced by an intense monsoon within the LMB (MRC 2015). This large, sustained flood pulse apparently led to a burst of recruitment by small fish species with opportunistic life-history strategies.

Hydrologically pulsing ecosystems in the tropics undergo significant changes in environmental
<figure><img src="image_4.png" /><figcaption>Fig. 2. Reference temporal changes of fish community structure in Tonle Sap Lake. Time series of MCOA synthetic scores on the first three axes from the reference fish abundance table. Gray line represents the daily water level fluctuation in the lake.</figcaption></figure>

conditions between wet and dry seasons, and these seasonal changes have been hypothesized to be the primary driver of fish community dynamics (Junk et al. 1989, Junk and Wantzen 2004). Several studies conducted in Neotropical river floodplains have documented variation in fish community structure, with temporal variation often less pronounced than spatial variation (Merigoux and Ponton 1999, Tedesco et al. 2008, Espírito-Santo et al. 2009, Scarabotti et al. 2011, Pease et al. 2012, Arantes et al. 2018). In Amazonian floodplains, water level was shown to be the most influential factor shaping fish abundance and community structure (Castello et al. 2015); however, water level was strongly correlated with other environmental factors, for example, temperature, oxygen, pH, and dissolved nutrient concentrations. Most studies focusing

on flood pulse systems have investigated temporal changes in diversity metrics, taxonomic composition, or trophic structure, with little attention to changes in the functional traits of communities (Brosse et al. 2007, Davidson et al. 2012, Chea et al. 2017, Kong et al. 2017, Piniewski et al. 2017). A study conducted in the Paraná River documented a reduction in biomass of large predators and a significant increase in abundance of small species during an extreme flood pulse (Agostinho and Zalewski 1995). Our study appears to be among the first to examine variation in functional structure of fish communities in relation to hydrology in a large pulsing system in Southeast Asia.

Black fishes with an equilibrium life-history strategy (*Trichogaster* spp.) dominated the TSL fish community during the low-water period,
<figure><img src="image_5.png" /><figcaption>Fig. 3. Relationships between functional traits and fish community structure in the Tonle Sap Lake. Only significant (*P* < 0.05 after correcting for multiple testing) relationships are shown. F1 axis presented the rough shift in community structure (size classes and trophic levels) caused by flood disturbance, while the seasonal patterns in fish community were observed on F3. The size of fish is in log scale and the full names of acronyms are provided in Appendix S1: Table S1, while MCOA scores are shown in Appendix S2: Table S1.</figcaption></figure>

and white fishes with an opportunistic strategy (*Paralaubuca* spp., *Henicorhynchus* spp.) had higher relative abundance near the peak of the flood pulse. Samples the ascending and descending limbs of the flood pulse were dominated by

gray and white fishes with a periodic life-history strategy (*Mystus* spp., *Puntioplites proctozystron*). According to the life-history strategy framework proposed by Winemiller (1989, 2005) and Winemiller and Rose (1992), opportunistic strategists
have short life spans, little or no parental investment, and multiple spawning events annually; thus, fish exhibiting opportunistic strategy are adapted to variable or harsh environments with unpredictable hydrology and capable of rapid population growth following flood disturbance as we observed in the TLS system (Winemiller and Rose 1992, Winemiller 2005). In contrast, equilibrium strategists have relatively low fecundity and high parental investment in individual offspring and often dominate in aquatic habitats with fairly stable perennial flows. Equilibrium strategists tended to be more abundant in the TLS during low-water periods, whereas opportunistic and periodic strategies tended to be less abundant. Periodic strategists in the TLS appear to be well adapted to seasonal hydrology and associated changes in environmental conditions in the riverscape. Periodic strategists, including large cyprinids and silurids, are characterized by delayed sexual maturation, high batch fecundity, contracted spawning periods, and episodic recruitment that occurs under suitable conditions. In the TLS, abundance of both opportunistic and periodic strategists was positively correlated with flow variability. Seasonal changes in the relative abundance of fish species with different life-history strategies observed in the TLS were consistent with previous observations documented in rivers of West Africa, North America, and the Amazon (Tedesco et al. 2008, Mims et al. 2010, McManamay and Frimpong 2015, Röpke et al. 2017).

In the TSL, we found that mud carp, gouramis and other small opportunistic species, were well adapted to exploit the heterogeneous and dynamic aquatic habitats in the surrounding floodplain, with their populations increasing rapidly during flow pulses. This observation is consistent with those from studies of fish assemblages in Neotropical floodplain rivers (Junk et al. 1989, Winemiller 1989, Merigoux and Ponton 1999, Agostinho et al. 2005, Tedesco et al. 2008). The increase of flood intensity during 2013 may have reduced natural and/or fishing mortality of small fishes because inundated riparian vegetation increases habitat structural complexity that provides refuge. The flood pulse also provides fishes with access to pools of food resources in expanding aquatic habitats of the floodplain. This mechanism was also highlighted

in a recent study on trophic niche plasticity in the lake, which showed the expansion of fish isotopic niches during the wet season, reflecting assimilation of resources from a wider range of sources (Pool et al. 2017). In our study, the mean and range of fish trophic position values varied seasonally. A similar seasonal isotopic shift was observed for the fish community of the Pantanal wetland in Brazil, with an increase in mean trophic position during the transition to the dry season, and greater relative abundance of herbivorous fishes during the wet season (Wantzen et al. 2002). In TSL, we found similar patterns, particularly after the peak of the flood pulse, more predators (e.g., *Notopterus notopterus*, *Pan-gasius* spp., *Hemibagrus nemurus*) were less common in our samples during high-water periods. An additional factor that may affect patterns of fish abundance is physical disturbance associated with flooding. Rapidly rising flood waters can damage spawning grounds and harm fish eggs and larvae with a negative effect on recruitment. Stocks of species with equilibrium and periodic-type life-history strategies would be expected to recover slower than those of smaller, opportunistic species with rapid life cycles and higher reproductive effort (Agostinho and Zalewski 1995, Welcomme and Halls 2004).

The timing, magnitude, and duration of the annual flood pulse clearly influence almost every aspect of the TSL ecosystem (Welcomme and Halls 2004, Castello et al. 2015). Greater magnitude and duration of flood events are predicted to increase fish diversity and yields (Junk and Wantzen 2004). Depending on the geomorphology, geology and other aspects of the riverscape, effects from floods may last for several years after the events. For example, Castello et al. (2015) documented effects from a flood pulse on multispecies fishery yields in the lower Amazon floodplains that endured for three years. Carlson et al. (2016) reported a six-year effect of flood disturbance on fish diversity and community structure in the Missouri River Delta. The timing of shifts in the TSL fish community documented in the current study would be difficult to predict during any given year. Seasonal trends in fish size and tropic position were generally consistent throughout the study period (Fig. 2). Long-term monitoring of the fish catch and environmental conditions will be required to determine whether
changes in response to extreme flood events persist or if there is a return to the previous state of the system and if so at which lag.

Fish migration patterns in Lower Mekong Basin have a strong influence in fish community structure of the TSL, with as many as one third of Mekong fishes (e.g., cyprinids and pangasiid catfishes) classified as white fish that undergo long-distance migrations between the lake and Mekong mainstream (Baran 2006, MRC 2007). Previous studies have documented how many TSL cyprinids (*Hypsibarbus* sp., *Puntioplites* sp., *Cyclocheilichthys* sp.) and catfishes (*Pangasius* sp., *Hemibagrus* sp.) begin their migrations at the end of the dry season and at the beginning of the wet season (Baran 2006, MRC 2007). Several months after the peak of the annual flood pulse and water begins to drain from TLS, cyprinids with more opportunistic-type life-history strategies (*Henicorhynchus* spp., *Labiobarbus* spp., *Paralaubuca* spp.) begin to dominate the fish catch. Black fishes (e.g., *Trichopodus* spp., *Clarias* spp., *Ananbas testudineus*, *Channa* spp.) are permanent residents of the lake and aquatic habitats in the floodplains, and some of these species have been reported to conduct lateral migrations between tributaries, floodplains, and the main body of the lake (Lamberts 2001, WorldFish 2007). Most of black fishes are carnivorous and can tolerate hypoxic conditions using accessory respiratory adaptations that allow them to obtain oxygen from air (Lamberts 2001, WorldFish 2007).

Generally, food resources are limited during the dry season, and many year-round resident fishes, that is, black and gray fish, feed primarily within the lake's littoral zone. During the annual flood pulse, nutrient inputs and aquatic ecosystem productivity increase (Kummu et al. 2006). In addition, fishes have greater access to allochthonous food resources within a greatly expanded littoral zone. Indeed, fish migrations in the Lower Mekong Basin and TSL appear to exploit spatial patterns and the pulsing nature of food resources. Consequently, temporal changes in fish community functional structure were expected to be stronger than changes in taxonomic structure per se.

As hypothesized, we found no significant relationship between temporal changes in the fish community structure and the geographical distance between sites. Previous studies on spatial

synchrony (intra- and interspecific) in Africa and Europe also have failed to reveal significant relationships between the level of synchrony in community dynamics and distance between locations (Grenouillet et al. 2001, Tedesco et al. 2004). For instance, the sites located close to each other (i.e., BB and SR, Fig. 1), presented low synchrony values (ρ). Overall, the temporal dynamics of fish assemblage structures across the five locations were quite synchronous, suggesting that habitats and fishes throughout the lake respond in a similar manner and sequence to hydrological variation. Nevertheless, two locations, that is, BB and KT, showed slightly low value of RV compared to other locations, reflecting less synchrony with the reference structure. These low values of RV for sites BB and KT could be explained by the influence of large tributaries near these locations, that is, Sangker River and Sen River, respectively (Fig. 1). These two tributaries have densely vegetated floodplains that could provide shelter for fishes (Kummu et al. 2014). Although the high degree of synchrony in local fish assemblage dynamics suggests that TSL fisheries might be managed as a singular unit, we caution that a sufficient number of exceptional locations suggests that different management strategies may need to be devised for regions of the lake by taking into account unique landscape features.

## Timing of changes in fish community structure in relation to hydrology

Major changes in fish community structure in relation to the peak of the annual flood pulse seemed to lag between one to two months (Appendix S3: Fig. S1). Several mechanisms could be responsible for time lags, including species reproductive biology and migratory behavior. Information is lacking for the reproductive physiology of fish native to the Lower Mekong Basin. In Brazilian river-floodplain systems, maturation of gonads in Characiform and Siluriform fishes was synchronized with the annual flood pulse (Chellappa et al. 2009, Röpke et al. 2019), but in floodplain systems in the Venezuelan Llanos and Central America, fishes from diverse taxa showed large interspecific variation in gonad maturation and reproduction in relation to pulsing hydrology (Winemiller 1989, 1993). In the Lower Mekong Basin, the first rainfall, which takes place about two months before the peak of
the pulse, was shown to be the key factor triggering fish movement, particularly for cyprinids (e.g., *Cyclocheilichthys enoplus*), catfish (e.g., *Pan-gasius larnaudii*, *Wallago attu*), presumably in response to the changes in physicochemical characteristics of water receiving runoff from the early rains (Baran 2006, MRC 2007). Therefore, time lags in the fish community response to hydrology could be explained partly by fish migration triggered by the first rainfall and associated factors, for example, conductivity, turbidity, temperature. Another factor that may contribute to time lags is interspecific differences in the speed of migration between the Mekong River and TSL that affects timing of arrival in the TSL. Although the magnitude and duration of the annual flood pulse varies interannually, the timing of flooding within the lake does not vary much between years; nonetheless, it could be significant if the upstream development is intensified; and most white fish migrate long distances, responding to cues in the Mekong River that induce entrance into the Tonle Sap River and the lake (Baran 2006). Alternatively, the time lags can be driven by ecosystem dynamics and the availability and accessibility of food resource at locations throughout the landscape. The flood pulse influences water quality, nutrient dynamics, decomposition of detrital pools, aquatic ecosystem productivity, and fish access to allochthonous resources. Further research is needed to determine if time lags in fish assemblage changes in response to the hydrological regime may be explained by species response to environmental cues (hydrology, water physico-chemistry) affecting migratory behavior and reproductive physiology/behavior along with the spatiotemporal dynamics of resource availability and accessibility on the landscape.

## CONCLUSIONS

The changes in the functional structure of TSL fish community in relation to the annual flood pulse were fairly consistent interannually, however, the extreme high magnitude flood of 2013 induced even greater change. The 2013 event reveals the potential for human alteration of the flow regime to affect fish ecology in this highly productive system. For example, development of hydropower in the Mekong mainstream likely

would alter the timing, magnitude, and duration of inundation of the floodplains that surround the lake. Depending on life-history strategies, trophic ecology, migratory behavior and other characteristics, some species likely would be negatively impacted by such changes, while a few might increase in abundance. Small species with rapid life cycles and an opportunistic life-history strategy might be resilient to an altered flow regime, but large migratory species with slower life cycles (white fish with a periodic life-history strategy) and black fish (relatively sedentary species) that inhabit aquatic habitats in shallow littoral areas of the lake would likely decline in abundance. Clearly, fisheries management in a large, complex, and dynamic ecosystem such as the TSL will require multiple strategies applied in different locations and evaluated using an adaptive management approach (Cooperman et al. 2012).

Finally, we found that changes in the functional structure of the TSL fish community to the hydrological variations lagged between 1 and 2 months before or after the peak of the pulse. This finding should be investigated further to determine if yields could be optimized by adjusting the timing and places for fishing in a manner that minimizes disruption to spawning and recruitment while exploiting post-spawn fish after a period of rapid somatic growth. Improved knowledge of how functional traits affect fish population and community responses to hydrological regimes would greatly enhance our ability to forecast fisheries production under alternative scenarios of fisheries exploitation, land-use, and climate.

## ACKNOWLEDGMENTS

The authors are grateful to Erasmus Mundus TECHNO Program and the Belmont Forum for providing the funds to support our study, which are part of the Tonle Sap Climate Change (TSLCC) project. We thank to the Mekong River Commission (MRC) for providing fish and hydrological data. Great thanks to our colleagues and friends, Mr. Phen Chheng, Dr. Les Kaufman, Dr. Lee Hannah for their insightful comments and discussion on our work.

## LITERATURE CITED

* Agostinho, A. A., L. C. Gomes, S. Veri, and E. K. Okada. 2005. Flood regime, dam regulation and
fish in the Upper Parana: effect on assemblage attributes, reproduction and recruitment. Reviews in Fish Biology and Fisheries 14:11–19.

Agostinho, A. A., and M. Zalewski. 1995. The dependence of fish community structure and dynamics on floodplain and riparian ecotone zone in Parana River, Brazil. Hydrobiologia 303:141–148.

Arantes, C. C., K. O. Winemiller, M. Petrere, L. Castello, L. L. Hess, and C. E. C. Freitas. 2018. Relationships between forest cover and fish diversity in the Amazon River floodplain. Journal of Applied Ecology 55:386–395.

Bady, P., S. Dolédec, B. Dumont, and J.-F. Fruget. 2004. Multiple co-inertia analysis: a tool for assessing synchrony in the temporal variability of aquatic communities. Comptes Rendus Biologies 327: 29–36.

Baran, E. 2006. Fish migration triggers in the Lower Mekong Basin and other tropical freshwater systems. Pages 70 MRC Technical Paper. MRC Technical Paper No. 14. Mekong River Commission.

Baran, E., N. So, S. V. Leng, R. Arthur, and Y. Kura. 2007. Relationships between bioecology and hydrology among Tonle Sap fish species. In Technical Assistance to the Kingdom of Cambodia for the Study of the Influence of Built Structures on the Fisheries of the Tonle Sap. Fisheries Component. TA 4669-CAM. Asian Development Bank.

Bower, L. M., and K. O. Winemiller. 2019a. Intercontinental trends in functional and phylogenetic structure of stream fish assemblages. Ecology and Evolution 9:13862–13876.

Bower, L. M., and K. O. Winemiller. 2019b. Fish assemblage convergence along stream environmental gradients: an intercontinental analysis. Ecography 42:1691–1702.

Brosse, S., G. D. Grossman, and S. Lek. 2007. Fish assemblage patterns in the littoral zone of a European reservoir. Freshwater Biology 52:448–458.

Carlson, A. K., M. J. Fincel, C. M. Longhenry, and B. D. S. Graeb. 2016. Effects of historic flooding on fishes and aquatic habitats in a Missouri River delta. Journal of Freshwater Ecology 31:271–288.

Carmel, Y., R. Kent, A. Bar-Massada, L. Blank, J. Liberzon, O. Nezer, G. Sapir, and R. Federman. 2013. Trends in ecological research during the last three decades - A systematic review. PLOS ONE 8: e59813.

Castello, L., V. J. Isaac, and R. Thapa. 2015. Flood pulse effects on multispecies fishery yields in the Lower Amazon. Royal Society Open Science 2:150299.

Chea, R., S. Lek, P. Ngor, and G. Grenouillet. 2017. Large-scale patterns of fish diversity and assemblage structure in the longest tropical river in Asia. Ecology of Freshwater Fish 26:575–585.

Chellappa, S., R. M. X. Bueno, T. Chellappa, N. T. Chellappa, and V. M. F. Almeida e Val. 2009. Reproductive seasonality of the fish fauna and limnoecology of semi-arid Brazilian reservoirs. Limnologica - Ecology and Management of Inland Waters 39:325–329.

Chessel, D., and M. Hanafi. 1996. Analyses de la co-inertia de Knuages de points. Revue Statistique Appliquée 44:35–60.

Clark, J., and O. Bjørnstad. 2004. Population time series: process variability, observation errors, missing values, lags, and hidden states. Ecology 85: 3140–3150.

Cooperman, M. S., et al. 2012. A watershed moment for the Mekong: newly announced community use and conservation areas for the Tonle Sap Lake may boost sustainability of the world's largest inland fishery. Cambodian Journal of Natural History 2012:101–106.

Davidson, T. A., A. W. MacKay, P. Wolski, R. Mazebedi, M. Murray-Hudson, and M. Todd. 2012. Seasonal and spatial hydrological variability drives aquatic biodiversity in a flood-pulsed, sub-tropical wetland. Freshwater Biology 57:1253–1265.

Dudgeon, D. 2003. The contribution of scientific information to the conservation and management of freshwater biodiversity in tropical Asia. Hydrobiology 500:295–314.

Dudgeon, D. 2011. Asian river fishes in the Anthropocene: threats and conservation challenges in an era of rapid environmental change. Journal of Fish Biology 79:1487–1524.

Dugan, P. J., et al. 2010. Fish migration, dams, and loss of ecosystem services in the Mekong basin. Ambio 39:344–348.

Espírito-Santo, H. M. V., W. E. Magnusson, J. Zuanon, F. P. Mendonça, and V. L. Landeiro. 2009. Seasonal variation in the composition of fish assemblages in small Amazonian forest streams: evidence for predictable changes. Freshwater Biology 54:536–548.

FEVM. 2007. Monitoring fish abundance and diversity in the Lower Mekong Basin: Methodological guidelines. Pages 12. Mekong River Commission, Phnom Penh, Cambodia.

Froese, R., D. Pauly, and editors. 2019. FishBase. World Wide Web electronic publication www.fishbase.org

Gelman, A., and D. Rubin. 1992. Inference from iterative simulation using multiple sequences. Statistical Science 7:457–511.

Grenouillet, G., B. Hugueny, G. A. Carrel, J. M. Olivier, and D. Pont. 2001. Large-scale synchrony and inter-annual variability in roach recruitment in the Rhone River: the relative role of climatic factors and density-dependent processes. Freshwater Biology 46:11–26.
Hortle, K. G. 2007. Consumption and the yield of fish and other aquatic animals from the Lower Mekong Basin. Pages 87. MRC Technical Paper No. 16. Mekong River Commission, Vientiane, Laos.

Humphries, P., H. Keckeis, and B. Finlayson. 2014. The river wave concept: integrating river ecosystem models. *BioScience* 64:870–882.

Junk, W. J., P. B. Bayley, and R. E. Sparks. 1989. The flood pulse concept in river-floodplain systems. Pages 110–127 *in* D. P. Dodge, editor. *Proceeding of the International Large River Symposium (LARS). Canadian Journal of Fisheries and Aquatic Sciences* 106.

Junk, W. J., and K. M. Wantzen. 2004. The Flood Pulse Concept: New aspect, approaches and application-An update. Pages 117–132 *in* R. L. Welcomme and A. T. Petr, editors. *Proceedings of the Second International Symposium on the Management of Large Rivers for Fisheries. Food and Agriculture Organization of the United Nations (FAO) and the Mekong River Commission (MRC), Bangkok*.

Kong, H., M. Chevalier, P. Laffaille, and S. Lek. 2017. Spatio-temporal variation of fish taxonomic composition in a South-East Asian flood-pulse system. *PLOS ONE* 12:1–16.

Kummu, M., J. Sarkkula, J. Koponen, and J. Nikula. 2006. Ecosystem management of the Tonle Sap Lake: an integrated modelling approach. *International Journal of Water Resources Development* 22:497–519.

Kummu, M., S. Tes, S. Yin, P. Adamson, J. Józsa, J. Koponen, J. Richey, and J. Sarkkula. 2014. Water balance analysis for the Tonle Sap Lake-floodplain system. *Hydrological Processes* 28:1722–1733.

Lamberts, D. 2001. Tonle Sap fisheries: a case study on floodplain gillnet fisheries in Siem Reap, Cambodia. Pages 133. FAO Regional Office for Asia and the Pacific, RAP Publication 2001/11, Bangkok, Thailand.

Liebhold, A., W. D. Koenig, and O. N. Bjørnstad. 2004. Spatial synchrony in population dynamics*. *Annual Review of Ecology, Evolution, and Systematics* 35:467–490.

Merigoux, S., and D. Ponton. 1999. Spatio-temporal distribution of young fish in tributaries of natural and flow-regulated sections of a neotropical river in French Guiana. *Freshwater Biology* 42:177–198.

McManamay, R. A., and E. A. Frimpong. 2015. Hydrologic filtering of fish life history strategies across the United States: implications for stream flow alteration. *Ecological Applications* 25:243–263.

Merigoux, S., S. Doledec, and B. Statzner. 2001. Species traits in relation to habitat variability and state:

neotropical juvenile fish in floodplain creeks. *Freshwater Biology* 46:1251–1267.

Mims, M. C., J. D. Olden, Z. R. Shattuck, and N. L. Poff. 2010. Life history trait diversity of native freshwater fishes in North America. *Ecology of Freshwater Fish* 19:390–400.

MRC. 2007. Fish migration triggers the Lower Mekong. Mekong Fisheries Management Recommendation No 6. Mekong River Commission. Vientiane, Laos PDR.

MRC. 2010. Assessment of basin-wide development scenarios: impacts on the Tonle Sap Ecosystem. Mekong River Commission, Vientiane, Lao PDR.

MRC. 2015. Annual Mekong Flood Report 2013. Theme: regional Impact of Tropical Storms. Page Mekong River Commission, Vientiane, Lao.

MRC. 2018. Updated MRC database estimates 1,148 fish species in Mekong Basin. Catch and Culture 25: Mekong River Commission Secretariat. Vientiane, Laos.

Ngor, P. B. 2012. Monitoring fish abundance and diversity around the Tonle Sap Lake. Catch and Culture 20:20–27.

Ngor, P. B., G. Grenouillet, S. Phem, N. So, and S. Lek. 2018. Spatial and temporal variation in fish community structure and diversity in the largest tropical flood-pulse system of South-East Asia. *Ecology of Freshwater Fish* 27:1087–1100.

Olden, J. D., M. J. Kennard, F. Leprieur, P. A. Tedesco, K. O. Winemiller, and E. García-Berthou. 2010. Conservation biogeography of freshwater fishes: recent progress and future challenges. *Diversity and Distributions* 16:496–513.

Ou, C., C. G. Montaña, and K. O. Winemiller. 2017. Body size–trophic position relationships among fishes of the lower Mekong basin. *Royal Society Open Science* 4:160645.

Ou, C., and K. O. Winemiller. 2016. Seasonal hydrology shifts production sources supporting fishes in rivers of the lower mekong basin. *Canadian Journal of Fisheries and Aquatic Sciences* 73: 1342–1362.

Pease, A. A., A. A. González-Díaz, R. Rodiles-Hernández, and K. O. Winemiller. 2012. Functional diversity and trait-environment relationships of stream fish assemblages in a large tropical catchment. *Freshwater Biology* 57:1060–1075.

Piniewski, M., C. Prudhomme, M. C. Acreman, L. Tylec, P. Oglecki, and T. Okruszko. 2017. Responses of fish and invertebrates to floods and droughts in Europe. *Ecohydrology* 10:1–17.

Pool, T. H., et al. 2017. Seasonal increases in fish trophic niche plasticity within a flood-pulse river ecosystem (Tonle Sap Lake, Cambodia). *Ecosphere* 8:1–15, e01881.
Pool, T. K., J. D. Olden, J. B. Whittier, and C. P. Paukert. 2010. Environmental drivers of fish functional diversity and composition in the Lower Colorado River Basin. Canadian Journal of Fisheries and Aquatic Sciences 67:1791–1807.

Poulsen, A. F., et al. 2004. Distribution and ecology of some important riverine fish species of the Mekong River Basin. MRC Technical Paper No. 10. ISSN: 1683-1489:1–116.

Poulsen, A. F., O. Poeu, S. Viravong, U. Suntornratana, and N. T. Tung. 2002. Fish migrations of the Lower Mekong River Basin: implications for development, planning and environmental management. Pages 62. MRC Technical Paper No. 8. Mekong River Commission, Phnom Penh, Cambodia. ISSN: 1683–1489:62.

R Core Team. 2019. R: A language and environment for statistical computing. R Core Team, Vienna, Austria.

Robert, P., and Y. Escoufier. 1976. A unifying tool for linear multivariate statistical methods: The RV-coefficient. Journal of the Royal Statistical Society: Series C (Applied Statistics) 25:257–265.

Röpke, C. P., S. Amadio, J. Zuanon, E. J. G. Ferreira, C. P. De Deus, T. H. S. Pires, and K. O. Winemiller. 2017. Simultaneous abrupt shifts in hydrology and fish assemblage structure in a floodplain lake in the central Amazon. Scientific Reports 7:1–10.

Röpke, C. P., T. H. S. Pires, K. O. Winemiller, D. de Fex Wolf, C. P. Deus, and S. Amadio. 2019. Reproductive allocation by Amazon fishes in relation to feeding strategy and hydrology. Hydrobiologia 826:291–305.

Santin-Janin, H., B. Hugueny, P. Aubry, D. Fouchet, O. Gimenez, and D. Pontier. 2014. Accounting for sampling error when inferring population synchrony from time-series data: a Bayesian state-space modelling approach with applications. PLOS ONE 9:e87084.

Scarabotti, P. A., J. A. López, and M. Pouilly. 2011. Flood pulse and the dynamics of fish assemblage structure from neotropical floodplain lakes. Ecology of Freshwater Fish 20:605–618.

Shumway, H. R., and S. D. Stoffer. 2015. Time Series Characteristics. Pages 1–25 *in Time series analysis and its applications*. EZ Edition. Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Simões, N. R., J. D. Dias, C. M. Leal, L. de Souza Magalhães Braghin, F. A. Lansac-Tôha, and C. C. Bonecker. 2013. Floods control the influence of environmental gradients on the diversity of zooplankton communities in a neotropical floodplain. Aquatic Sciences 75:607–617.

So, N., S. Phommakone, L. Vuthy, T. Samphawamana, N. H. Son, M. Khumsri, N. P. Bun, K. Sovanara, P. Degen, and P. Starr. 2015. Lower Mekong fisheries estimated to be worth around $17 billion a year. Catch and Culture 21:4–7.

Sutherland, et al. 2013. Identification of 100 fundamental ecological questions. Journal of Ecology 101:58–67.

Tedesco, P. A., B. Hugueny, T. Oberdorff, H. H. Dürr, S. Mérigoux, and B. de Mérona. 2008. River hydrological seasonality influences life history strategies of tropical riverine fishes. Oecologia 156:691–702.

Tedesco, P. A., B. Hugueny, D. Paugy, and Y. Fermon. 2004. Spatial synchrony in population dynamics of West African fishes: a demonstration of an intraspecific and interspecific Moran effect. Journal of Animal Ecology 73:693–705.

Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell, and C. E. Cushing. 1980. The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37:130–137.

Venables, W. N., and B. D. Ripley. 2002. Time series analysis. Pages 387–414 *in* Modern Applied Statistics with S. Fourth edition. Springer-Verlag, Berlin, Germany.

Vörösmarty, C. J., et al. 2010. Global threats to human water security and river biodiversity. Nature 467:555–561.

Wantzen, K. M., F. D. A. Machado, M. Voss, H. Boriss, and W. J. Junk. 2002. Seasonal isotopic shifts in fish of the Pantanal wetland. Brazil 64:239–251.

Welcomme, R. L., and A. Halls. 2004. Dependence of tropical river fisheries on flow. Pages 267–283 *in* R. L. Welcomme and A. T. Petr, editors. Proceedings of the second international symposium on the management of large rivers for fisheries II. Volume 2. FAO, Bangkok.

Winemiller, K. O. 1989. Patterns of Variation in Life History among South American Fishes in Seasonal Environments. Oecologia 241:225–241.

Winemiller, K. O. 1993. Seasonality of reproduction by liverbearing fishes in tropical rainforest streams. Oecologia 95:266–276.

Winemiller, K. O. 2005. Life history strategies, population regulation, and implications for fisheries management. Canadian Journal of Fisheries and Aquatic Sciences 62:872–885.

Winemiller, K. O., et al. 2016. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351:128–129.

Winemiller, K. O., D. B. Fitzgerald, L. M. Bower, and E. R. Pianka. 2015. Functional traits, convergent evolution, and periodic tables of niches. Ecology Letters 18:737–751.
Winemiller, K. O., and K. A. Rose. 1992. Implications for population regulation. Canadian Journal of Fisheries and Aquatic Sciences 49:2196–2218.

WorldFish. 2007. Technical assistance to the Kingdom of Cambodia for the study of the influence of build structures on the fisheries of the Tonle Sap. Phnom Penh, Cambodia.

Ziv, G., E. Baran, S. Nam, I. Rodríguez-Iturbe, and S. A. Levin 2012. Trading-off fish biodiversity, food security, and hydropower in the Mekong River Basin. Proceedings of the National Academy of Sciences of the United States of America 109:5609–5614.

## SUPPORTING INFORMATION

Additional Supporting Information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecs2.3303/full