Abstract
Introduction:
This study examines the dynamics and characteristics of seasonal fish migration into the floodplain of a large lowland river.
Methods:
For continuous recording of the number of fish migrating upstream and downstream, and their size composition, the hydroacoustic complex “NetCor” was used. The system was permanently installed in spring 2022 on a floating platform in the Varpak River (a small tributary of the Irtysh River), mounted in a side-scan setup from the left bank.
Results:
The dominant role of water level and temperature for abundance dynamics of fish migrating into the floodplain is demonstrated. The earlier onset of high values for these factors positively correlate with the abundance of migratory fish. Their influence accounts for approximately 70%. On a daily basis, migration dynamics are determined by the daily variation in illumination. The abundance of upstream migrant fish correlated with diurnal variations in illumination. Migration intensity increases during periods preceding morning and, to a lesser extent, evening twilight. The lowest proportion of migratory fish was observed during the daytime, when illumination was highest.
Discussion:
The influence of water temperature, water level and light on the dynamics of seasonal fish migration is revealed. At the same time, a number of specific features have been noted for seasonal migration to the floodplain: synchronous increases and decreases in the intensity of fish movements up and downstream are simultaneously recorded, while the general trend of migration to the floodplain is maintained; in the initial phase of the flood, the largest fish are the first to migrate into the floodplain, which is an adaptive behavioral response. The diurnal distribution pattern of migratory fish is a strategy which they use to counter visually oriented predatory fish species—zander, perch, and pike—to avoid optimal prey search conditions.
1 Introduction
The spatial distribution of freshwater fishes is determined by various factors: physicochemical parameters of the aquatic environment (Buisson et al., 2008; Nannini et al., 2012; Muška et al., 2018; Tucker et al., 2019), resource and habitat availability (Górski et al., 2012; Nannini et al., 2012), interactions in the predator-prey system (Mikheyev et al., 2002; Brown, 2003; Lloren et al., 2019) as well as intraspecific relationships and individual preferences (Gerber et al., 2018).
Fish distribution dynamics are observed as a result of their migration, a common evolutionary behavioral adaptation of many animals that occurs in response to factors. Fish migrations are observed at various stages of their life cycle, between and within different habitats (Morais and Daverat, 2016). The purpose of migration is to access living space and resources that ensure maximum growth, shelter from predators, survival, and reproductive success (Louca et al., 2009; Mehner, 2012; Nakamura et al., 2015; Nordahl et al., 2019; Tamario et al., 2019; Senner et al., 2020). Migration strategies of fish are a combination of a wide range of complex and flexible elements of their behavior (Tamario et al., 2019).
Fish migration is most pronounced in seasonal habitats, which include river floodplain-channel systems. Large river floodplain-channel systems are characterized by seasonal variability in runoff, water level, and flooded area. Floods and high waters are of fundamental importance for the formation of aquatic communities; matter, energy and organisms are transferred between the riverbed and the floodplain (Chang et al., 2025), a hydrological connection is formed between the main channel and floodplain water bodies, which are critical habitats for river biota (Górski et al., 2013; Chang et al., 2025). In floodplains, huge areas of feeding and spawning grounds for fish populations are being formed (Górski et al., 2013; Minina et al., 2025).
In a riverbed–floodplain system, tributaries are fundamentally important for realizing life cycle of many fish species (Spurgeon et al., 2018). The importance of tributaries for fish reproduction, feeding, and growth has not been studied in sufficient detail compared to main rivers (Naus and Adams, 2016).
If, in the daily aspect, as a rule, the dynamics of illumination is observed, then with seasonal dynamics, the floodplain-channel complex undergoes significant changes regarding hydrology and temperature. These factors have a significant impact on the dynamics of fish populations in the floodplain-channel systems of large rivers and their tributaries (Dutterer et al., 2013; Espínola et al., 2016; Walton et al., 2017). Currently, there is a lack of information on the mechanisms by which these factors influence migrations into the floodplains of large rivers (Górski et al., 2012, Górski et al., 2013), especially of fish that do not belong to the family Salmonidae that have been most studied in this regard (Walton et al., 2017). Thus, the study of the mechanisms of distribution, migration and dynamics of fish in the floodplain-channel system is an important, relevant and little-studied area of modern ichthyological research (Louca et al., 2009; Górski et al., 2013; Naus and Adams, 2016; Stoffers et al., 2022b, Stoffers et al., 2025). Floodplain-channel ecosystems are, on the one hand, economically valuable and highly productive (Van de Wolfshaar et al., 2011; Toruan et al., 2022; Rezek et al., 2023), on the other hand, they are ecologically vulnerable. Understanding the functioning and role of all types of water bodies depending on the season and environmental factors will allow us to more accurately predict the extent of damage and negative consequences for the fisheries industry as a result of anthropogenic impact on the components of the floodplain-channel ecosystem (Vaughan et al., 2009; Goulding et al., 2018).
In floodplain-channel complexes, the biotopic heterogeneity of the watercourse is very dynamic with fluctuations in the water level in the channel (Mignien and Stoll, 2024; Ramsey et al., 2024), this feature is also characteristic of the floodplain. Its “biological pulsation”, caused by high-amplitude fluctuations in water level and temperature regimes, intensifies the development of the food supply and contributes to an increase in the biodiversity of the biota (De Lima et al., 2023; Yanygina et al., 2025; Chang et al., 2025) when the water level rises, and when it falls, conversely, fish migrate and aquatic organisms migrate back to the main channel. All of this reflects the multifaceted, heterogeneous, and varied nature of the constituent components of river basins with developed floodplains.
Thus, floodplain-channel complexes are critically important habitats for fish populations. Despite this, 90% of floodplain areas in Europe and North America are considered effectively lost and dysfunctional as a result of anthropogenic impacts (Górski et al., 2010; Stoffers et al., 2022a, Stoffers et al., 2022b). Untransformed floodplains of large rivers and their channels with reaches, pits, high curvature, developed meandering, the presence of hydrodynamic shadow zones reveal a biotopically heterogeneous picture of the floodplain-channel system, which provides key habitats for many fish species in rivers of both tropical and temperate climates (Górski et al., 2011; Stoffers et al., 2022b, Stoffers et al., 2025).
The floodplain-channel complex of the Lower Irtysh remains intact and serves as a suitable model for studying the behavioral response of fish populations to seasonal environmental changes in conditions of heterogeneity of floodplain-channel systems and dynamically changing environmental factors. Determining the influence of environmental factors on the nature, features of migration and distribution of fish contributes to obtain expanded knowledge about floodplain-channel complexes аnd its are the basis for preserving their integrity and biodiversity and are important tasks of modern ecological and ichthyological research (Katopodis, 2012; Borisenko et al., 2013; Kluender et al., 2017; Tamario et al., 2019; Senner et al., 2020).
2 Methods
The studies were conducted immediately after ice drift (ice breakup on 20.04.2022) on a small tributary of the Irtysh River, between April 28, and May 29, 2022. The Varpak River is a right-bank tributary of the Irtysh River, located 517 km from the mouth of the Irtysh River, and belongs to the Irtysh River water management area (from the confluence of the Tobol River to the city of Khanty-Mansiysk (above), excluding the Konda River [code in the water register = 14010700112115300013098, see https://textual.ru/gvr/index.php?card=197921]. The mouth of Varpak River is located at the coordinates 58.721153° N, 68.684185° E (Figure 1). The length of river is 14 km, during the flood period in its pre-mouth zone it forms a limnic area with a floodplain. Due to the backwater of the Irtysh River, there is no current at the lower stretch of Varpak River; water movement occurs solely due to wind. Currents occur when the water level in the Irtysh River drops. The channel width in the monitored area, depending on the water level, reaches 25–30 m and the depth 10–12 m.
Figure 1
Illumination was measured with a Yu-116 lux meter in lux (lx) in 1-hour increments (hourly). Temperature (Varpak River, Irtysh River) and water level (Irtysh River) were measured daily. Water temperature was determined using a Horiba-U22 multiparameter water quality assessment system (Horiba Inc., Japan). Water levels were measured daily at the mouth of the Varpak River using a measuring rod. The water level measurement taken on the first day of the study was considered to be “zero.” Every subsequent day, a daily increase was added to this indicator, which was calculated as the difference between the level marks made on the day of the study and the day before. The flow velocity in the Irtysh and Varpak rivers was not measured.
For continuous recording of the number of fish migrating upstream and downstream, and their size composition, the hardware hydroacoustic complex “NetCor” as well as the corresponding software NetCor 2014 (OOO “Promgidroakustika”, Petrozavodsk) was used.
The system was permanently installed on a floating platform in the watercourse, mounted in a side-scan setup from the left bank. The hydroacoustic system monitored the entire cross-section of the watercourse, covering an area of approximately 8.75 square meters. The system assessed daily and monthly dynamics of fish migration, its direction, and the size structure of migrants. The platform installation location is shown in Figure 1. The recording and transmitting parts are secured to hydroacoustic buoys, which are installed in the watercourse using self-made anchors (Figure 1). The nylon cords used to secure the buoy to the anchor are prepared with extra length, and the cord is fixed to the bollards located at the front and rear of the buoy.
As the water level in a watercourse fluctuates, excess tension occurs when the water level rises, or excess slack occurs when the water level falls. Therefore, during these periods, the length reserve was increased or decreased from the small vessel’s side.
Each of the three slave recording units, after converting data through an analog-to-digital converter (ADC), transmits digital information about the time of fish registration, their number and size to the master unit via a 2.4 GHz ISM radio channel, which ensures stable communication at a distance of up to 200 m. Each recording and receiving unit is powered by 12-volt lead-acid batteries mounted on floating platforms. The platform is equipped with a hydroacoustic transmitter antenna (transducer) with an operating frequency of 455 kHz and six 10° beams, performing horizontal scanning at a range of up to 20 meters. The transducer and transmitter are mounted on a rotating, retractable device mounted on the buoy. This device allows for adjustment of the scanning beam’s angle of incidence into the water column and its depth relative to the water surface.
The “slave” unit is launched by the “master” unit via a connected Xplore 103,104i (USA) field tablet. A USB flash drive is installed on the “Master” unit, onto which the sonar data is recorded as individual files with a one-hour timeframe. To ensure uninterrupted operation during stationary sonar surveys, the battery on one platform was replaced daily using a small vessel. The removed battery was charged, and a charged one was installed in its place. To avoid interrupting the formation of an hour-long hydroacoustic survey file, the battery was replaced at the beginning or end of the hour (changed every 48 h). When the USB flash drive on the Master module was full, it was replaced with a free one, and the one removed from the system was emptied. The generated echo recording files were decrypted using the post-processing program “NetCor” (e.g. Supplementary Materials A – D). The specified program provides for the analysis of hydroacoustic data, including noise filtering and cluster analysis to differentiate the fish signal from various reverberations (surface, bottom, heterogeneity of the aquatic environment due to debris and bubbles). As a result, it became possible to identify and track individual fish passing through the flow cross-section covered by the multibeam transducer signal, and to identify the direction of movement and the target strength in decibels (dB). These reflected sound characteristics were converted into body length using the classic Love formula: TS = 19.1*lg(L) - 0.9*Lg(F)-62.0 (Love, 1977), where TS = target strength (dB), L = length of the fish (cm) and F = frequency (KHz). For analysis, the resulting size ranges of migratory fish were grouped into size groups >5, 5-10, 10-15, 15–20 cm, etc., depending on the objectives. The acoustic parameters of the equipment were pre-calibrated using a standard copper sphere with a diameter of 39.6 mm (Simmonds and MacLennan, 2005).
Statistical data processing was performed in Statistica 12 (StatSoft Inc., USA). To assess the significance of differences between indicators (data series), the Tukey HSD Test was used for equal and unequal samples, depending on the sample’s compliance with the specified conditions. To study the relationships between the abundance of migratory fish and the dynamics of water level and temperature factors, cross-correlations were calculated (Wessa, 2025). To calculate the cross-correlation function, the analyzed data were normalized using the Box-Cox transformation parameter. To select a statistical model characterizing the seasonal migration of fish into the floodplain, an analysis of the autocorrelation function was preliminarily carried out for a number of independent variables (the daily number of migrant fish).
3 Results
According to the test catch data, it was established that the dominant part of the migrant fish is represented by Cyprinidae and Percidae – roach (Rutilus rutilus), ide (Leuciscus idus), bream (Abramis brama), crucian carp (Carassius gibelio) and dace (Leuciscus leuciscus), perch (Perca fluviatilis) and pike perch (Sander lucioperca). As a result of the analysis of the dynamics of the average number of migrant fish (thousand specimens/day) in the studied period of spring fish migration to the Irtysh floodplain (polynomial function), it was possible to identify the beginning of migration and growth in the number of migratory fish (28.04-30.04), and also 2 migration peaks - 1st (1.05-18.05), 2nd migration peak (19.05-29.05). Thus, the specified 3 periods were further analyzed (Figure 2).
Figure 2
The average daily abundance of recorded migrant fish for observation periods I, II, and III was 2,660, 8,750, and 8,930, respectively (Figure 3). Statistical analysis of variance revealed a significant difference in migrant abundance between the migration periods under consideration (F = 10.965; p<0.001). Furthermore, Tukey’s test revealed a significant difference in the abundance of migratory fish: the first period (the beginning of migration) significantly differed from the second (p=0.003) and third (p=0.006). Thus, a significant difference was demonstrated between the peaks of migratory activity and the period of its beginning. Water temperatures at the end of each of the three identified time intervals during the spring flood were 3.33, 11.55, and 15.34 °C, respectively. For water level, the corresponding values for the observation periods (cumulative total) were 0.345, 1.355, and 1.679 m (Figure 3). An analysis of the autocorrelation function (ACF) for a series of observations of the daily abundance of migratory fish showed that the ACF values range from -0.4 to 0.56. For lags of 1 and 2, the ACF values are 0.56 and 0.32. For a positive lag of up to 10, a “fading” and a shift of values into the negative zone are observed, which are signs of a process with a “short memory” and indirectly confirm the linearity of the data, as well as the absence of long-term cyclical effects (Table 1). Analysis of water level and temperature factors in the Irtysh River showed that these factors are multicollinear (VIF>10), which makes it difficult to determine the differentiated effect of each factor. At the same time, the goal of our study is to determine the overall influence of the abiotic factors under consideration on the intensity of seasonal fish migration, rather than to identify and separate their independent effects and make predictions. This allows us to use a simple multiple-regression model for our analysis. However, we additionally conducted modeling using a generalized linear model (GLM) to assess the reliability of the factors’ influence and their overall impact on the intensity of seasonal fish migration.
Figure 3
Table 1
| Time lag k | ACF(k) | T-stat | p-value |
|---|---|---|---|
| 1 | 0.56 | 3.21 | <0.05 |
| 2 | 0.32 | 1.85 | <0.05 |
| 3 | 0.24 | 1.38 | 0.09 |
| 4 | 0.00 | 0.00 | 0.50 |
| 5 | 0.01 | 0.04 | 0.48 |
| 6 | -0.05 | -0.31 | 0.38 |
| 7 | -0.26 | -1.48 | 0.07 |
| 8 | -0.16 | -0.94 | 0.18 |
| 9 | -0.14 | -0.79 | 0.22 |
| 10 | -0.40 | -2.31 | <0.05 |
| 11 | -0.28 | -1.58 | 0.06 |
| 12 | -0.16 | -0.90 | 0.19 |
| 13 | -0.06 | -0.37 | 0,36 |
| 14 | 0.13 | 0.74 | 0,23 |
| 15 | 0.14 | 0.83 | 0,21 |
Estimation of the autocorrelation function (ACF) for series of observations of the number of migrant fish per day (28.04-30.05.2022), after lag 15 the correlation is outside the confidence interval.
Moreover, a number of authors indicate that temperature and hydrological factors are among the dominant ones in initiating seasonal fish migration (Jonsson, 1991; Hohausová et al., 2003; Górski et al., 2010; Pavlov et al., 2021). In this regard, we use these factors in the subsequent regression analysis, with a significance level confirming their reliable effect (p<0.05), despite their multicollinearity.
In the case of a small Varpak tributary of the Irtysh River, a multiple regression analysis of the response of migratory fish abundance to environmental factors was used to consider water temperature in the tributary, in the Irtysh River, and the daily and rising water level in the Irtysh River. The significance level of the influence of these factors in multiregressive analysis and GLM was p=0.269, p=0.004, p<0.001 and p=0.002, respectively (Table 2). The comparability of the assessment of the reliability of the influence of factors confirms the adequacy of the selection of the first and second models.
Table 2
| Factors | significance level (p) | coefficient of determination (R2) | |
|---|---|---|---|
| MRS | GLM | ||
| water level in the Irtysh river (cumulative total) | 0.002 | 0.654 | 0.655 |
| daily increase in water level in the Irtysh River | <0.001 | ||
| water temperature in the Irtysh River Water | 0.004 | ||
| water temperature in the Varpak river | 0.269 | ||
Results of the analysis of the influence of environmental factors (variables) on the abundance of migrant fish (multi-regression (MRS) analysis and GLM).
Thus, it was established that during the migration of fish into a small tributary during the specified period, the daily indicator, the cumulative total of the water level and the temperature of the Irtysh River have a reliable influence, and the analysis of variance (ANOVA) confirms the acceptability of the analysis performed (p<0.001). At the same time, the calculated determinant indicator (R2), or the share of influence of these factors on the response expressed in the number of migrant fish, in multiregressive analysis and GLM was 0.654 and 0.655, respectively (F = 13.27, p<0.001) (Table 2) i.e., it exceeded the value of 0.300, which in turn reflects the adequacy of the choice of the factors under consideration. The value of the determinant close to 1 allows us to consider the indicated factors in this case as the main and dominant ones. At the same time, the temperature factor in the Varpak River should not be completely excluded from the group of influencing factors, since, in our opinion, to a certain extent it also influences the intensity of seasonal fish migration into this tributary.
Further qualitative analysis of the regression model revealed that the frequency histogram of the residuals is symmetrical, and their distribution plot is normal (the actual values do not systematically deviate from the theoretical line). Thus, the residuals of the resulting regression model are normally distributed, and the scatterplot shows that the residuals are independent of the predicted response values according to the regression equation, which also confirms the acceptability of the simple multiple regression model.
An analysis of the Durbin-Watson (DW) exponent in the model residuals revealed a value of 1.73, falling within the range of 1.5≤DW ≤ 2.5, confirming the absence or insignificance of autocorrelation and the adequacy of the model. A determination of the serial correlation of the residuals revealed that it is very insignificant (r=0.086) and does not significantly impact the quality of the regression model, which is correct, and also confirms the absence of significant first-order autocorrelation. Thus, the adequacy and acceptability of using multiple regression analysis to assess the influence of abiotic factors on the intensity of seasonal migration has been repeatedly confirmed.
A cross-correlation analysis of the relationship between migratory fish abundance and environmental factors revealed a number of patterns. The moderate positive correlation between migratory fish abundance and water level was strongest for time lags ranging from 1 day ago to 4 days ahead. Moreover, 80% of the specified period falls within the positive lag. The indicator value was r = 0.34-0.43. At the same time, a weak negative correlation can be observed with a time lag of 9–10 days, r = -0.27-0.28 (Figure 4A).
Figure 4
When analyzing the relationship with the daily increase in water level, it was shown that its greatest negative value was noted with a time lag of the studied factor from 1 day ago to 3 days ahead (Figure 4B). The correlation coefficient (r) varied from -0.45 to -0.68. In the study period (28.04.-30.05.2022), the largest three-day set of daily water level increase values was recorded at the beginning of the observation period (28.04.-30.04.2022). A forward shift with a shift specifically for these dates indicates that, with low flood intensity and diurnal water rise, conditions for fish migration into the floodplain will be unacceptable and virtually nonexistent. Consequently, no fish migration will be recorded in the studied tributary, and the number of migrant fish will decrease. Intensified migration occurs when the floodplain becomes saturated and hydrological connectivity develops. At the same time, low water levels lead to a decline in the number of fish migrating into the floodplain due to a lack of necessary conditions for their migration.
A positive correlation between migratory fish abundance in the Varpak River and water temperature in the main watercourse, the Irtysh River, was observed. The highest moderate-strength temperature was observed in the period from 0 to 4 days ahead, r = 0.34-0.45 (Figure 4C). The similarity of the influence of the above factors (temperature and water level in the Irtysh River) on the abundance of migratory fish shows their combined unidirectional effect on fish. At the same time, these factors are highly synchronized and correlated with each other (Rs=0.99, p<0.05). For the temperature factor in the Varpak River, the trend and general trend are similar. The highest indicators of moderate positive correlation with the number of migratory fish were noted with a positive time lag of up to 6 days, r=0.37–0.49 (Figure 4D).
An analysis of the direction of fish migration in a small tributary revealed heterogeneity and the absence of a strict direction of migrating fish downstream or upstream.
At the same time, the general trend and numerical dominance of fish species migrating to the river’s floodplain complex (upstream) persists. The average proportion of upstream migrants was 61.25%, while downstream migrants accounted for 38.75% of the total number of recorded fish.
As a result of the analysis of the distribution of the daily course of migratory fish, it was established that the dominant part of migratory fish, similar to the seasonal migration in a large tributary both downwards and upwards, is recorded during daylight hours, and a smaller part – during the twilight-night period. During the upstream migration of fish, the share of migrants during daylight hours from the total number of fish migrating to the flooded floodplain was 60.67%, from the floodplain – 61.56% (Figure 5).
Figure 5
Statistical analysis, similar to spring migration in the Konda River (Chemagin and Schletterer, 2023), revealed the presence of a direct, significant correlation between the intensity of fish migration upstream and the illumination index (RS = 0.62; p<0.05). The strength of the correlation of the same level and direction was found between the general indicator of the intensity of fish migration downstream and upstream with the illumination indicator (RS = 0.54; p<0.05). For the intensity of fish migration downstream, no connection with the daily variation in illumination was found (RS = 0.11; P<0.05).
It’s worth noting that fish migration intensity, in daily terms, peaks in the period preceding morning twilight (2:00-6:00 AM). It decreases during the period of maximum illumination (10:00-14:00 AM), then increases slightly again in the period preceding evening twilight (18:00-20:00 PM) (Figure 5).
The analysis of the autocorrelation and partial autocorrelation functions of the diurnal cycle of migrant fish revealed the presence of a relationship (ACF) at lags <3 and the absence of a relationship (PACF) at lags >1. This confirms not the inertia and coherence of the process (dependence of samples), but the direction of the overall dynamics and cyclicity caused by the diurnal cycle of illumination, i.e., the samples are independent (Table 3). Therefore, we further used Tukey’s test to assess the reliability of differences in the observation series between migration directions and periods of the day. Statistical analysis based on the Tukey criterion (at p<0.05) revealed the presence of a significant difference between the average daily number of fish migrants upstream and downstream (Figure 6).
Table 3
| Time lag k | ACF(k) | T-STAT | p-value | ACF(k) | T-STAT | p-value | PACF(k) | T-STAT | p-value | PACF(k) | T-STAT | p-value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fish migrating upstream | Fish migrating downstream | Fish migrating upstream | Fish migrating downstream | |||||||||
| 1 | 0.73 | 3.56 | 0.00 | 0.60 | 2.95 | 0.00 | 0.73 | 3.56 | 0.00 | 0.60 | 2.95 | 0.00 |
| 2 | 0.42 | 2.08 | 0.02 | 0.38 | 1.86 | 0.04 | -0.22 | -1.08 | 0.14 | 0.03 | 0.13 | 0.45 |
| 3 | 0.24 | 1.19 | 0.12 | 0.14 | 0.67 | 0.25 | 0.06 | 0.30 | 0.38 | -0.16 | -0.78 | 0.22 |
| 4 | 0.05 | 0.24 | 0.40 | -0.06 | -0.29 | 0.39 | -0.22 | -1.06 | 0.15 | -0.15 | -0.71 | 0.24 |
| 5 | -0.14 | -0.69 | 0.25 | -0.15 | -0.74 | 0.23 | -0.14 | -0.66 | 0.26 | -0.03 | -0.16 | 0.44 |
| 6 | -0.29 | -1.43 | 0.08 | -0.20 | -0.96 | 0.17 | -0.16 | -0.76 | 0.23 | -0.04 | -0.20 | 0.42 |
| 7 | -0.33 | -1.63 | 0.06 | -0.32 | -1.57 | 0.06 | 0.02 | 0.10 | 0.46 | -0.25 | -1.22 | 0.12 |
| 8 | -0.37 | -1.80 | 0.04 | -0.28 | -1.39 | 0.09 | -0.18 | -0.86 | 0.20 | 0.01 | 0.04 | 0.48 |
| 9 | -0.35 | -1.71 | 0.05 | -0.23 | -1.14 | 0.13 | 0.03 | 0.13 | 0.45 | 0.01 | 0.03 | 0.49 |
| 10 | -0.26 | -1.26 | 0.11 | -0.21 | -1.01 | 0.16 | -0.01 | -0.07 | 0.47 | -0.11 | -0.53 | 0.30 |
| 11 | -0.19 | -0.91 | 0.18 | -0.10 | -0.48 | 0.32 | -0.08 | -0.38 | 0.35 | 0.01 | 0.07 | 0.47 |
| 12 | -0.08 | -0.38 | 0.35 | -0.05 | -0.24 | 0.40 | 0.07 | 0.37 | 0.36 | -0.04 | -0.19 | 0.43 |
| 13 | 0.04 | 0.20 | 0.42 | 0.05 | 0.22 | 0.41 | 0.01 | 0.05 | 0.48 | 0.07 | 0.36 | 0.36 |
| 14 | 0.09 | 0.44 | 0.33 | 0.16 | 0.79 | 0.22 | -0.09 | -0.43 | 0.33 | 0.07 | 0.35 | 0.36 |
| 15 | 0.10 | 0.49 | 0.32 | 0.13 | 0.61 | 0.27 | -0.03 | -0.15 | 0.44 | -0.13 | -0.64 | 0.26 |
| 16 | 0.10 | 0.51 | 0.31 | 0.02 | 0.09 | 0.46 | -0.01 | -0.07 | 0.47 | -0.17 | -0.83 | 0.21 |
| 17 | 0.05 | 0.26 | 0.40 | -0.01 | -0.05 | 0.48 | -0.16 | -0.78 | 0.22 | 0.01 | 0.07 | 0.47 |
| 18 | -0.08 | -0.40 | 0.35 | -0.19 | -0.92 | 0.18 | -0.17 | -0.84 | 0.20 | -0.23 | -1.13 | 0.13 |
| 19 | -0.17 | -0.83 | 0.21 | -0.12 | -0.58 | 0.28 | -0.05 | -0.24 | 0.41 | 0.11 | 0.56 | 0.29 |
| 20 | -0.06 | -0.29 | 0.39 | -0.07 | -0.34 | 0.37 | 0.30 | 1.65 | 0.07 | 0.04 | 0.18 | 0.43 |
| 21 | 0.00 | 0.02 | 0.49 | 0.01 | 0.04 | 0.48 | -0.10 | -0.47 | 0.32 | 0.10 | 0.50 | 0.31 |
| 22 | -0.01 | -0.07 | 0.47 | -0.01 | -0.03 | 0.49 | -0.01 | -0.05 | 0.48 | -0.15 | -0.76 | 0.23 |
| 23 | -0.01 | -0.03 | 0.49 | 0.01 | 0.04 | 0.48 | -0.10 | -0.47 | 0.32 | -0.06 | -0.28 | 0.39 |
Estimation of the autocorrelation function (ACF) and partial autocorrelation function (PACF) for series of observations of the daily migration of fish (00.00-23.00 h).
Figure 6
No significant difference in the average proportion of migrant fish was observed between the periods of the day (day and twilight-night) for fish migrating downstream (p=0.531). For upstream migrants, a significant difference was found between these periods of the day (p<0.05) (Figure 6). An analysis of the size structure of migrating fish over observation periods revealed a trend of decreasing proportions of larger fish (>35 cm) and increasing proportions of medium-sized and smaller fish (<35 cm) both upstream and downstream (Figure 7). In the first observation period, among the fish migrating to the floodplain, the proportion of the group of large fish (>35 cm) was 24.18%, and the proportion of fish with a body length of <35 cm was 75.82% (Figure 7). In spring about 70% of the recorded fish are Cypriniformes, 20% Perciformes and 10% Esociformes (Chemagin, 2020).
Figure 7
For floodplain migrants, these values (>35 cm and <35 cm) were 22.52 and 77.48%, respectively. Upstream migrating fish were distributed among size groups <5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45-50, and >50 cm as follows: 10.58, 15.8, 12.68, 12.19, 9.78, 8.5, 6.29, 4.13, 6.07, 6.98, and 7%, respectively. Among the migrant fish from the floodplain, the distribution by size groups <5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45–50 and >50 cm was similar – 11.7, 16.52, 13.95, 14.11, 9.52, 7.04, 4.64, 4.41, 5.32, 5.99 and 6.8%, respectively.
In the second observation period, among individuals migrating to the floodplain, the proportion of larger fish (>35 cm) decreased and its value was 17.48%; among fish migrants from the floodplain, this proportion was 17.16%.
The size distribution of fish <5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45–50 and >50 cm migrating upstream was 11.38, 16.1, 15.32, 13.91, 11.78, 8.03, 6 4.32, 4.07, 4.98 and 4.11%, respectively. Among the migrant fish from the floodplain, the distribution by size groups <5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45–50 and >50 cm was also similar: 12.9, 17.52, 14.97, 14.39, 11.33, 6.77, 4.96, 4.11, 4.32, 4.85 and 3.88%, respectively.
In the third period, the trend of decreasing proportion of larger individuals did not change; its value continued to decrease, while the proportion of smaller fish increased, both upstream and downstream. The distribution of fish <5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45–50 and >50 cm migrating upstream by size groups was: 10.95, 16.53, 15.5, 14.11, 12.07, 9.15, 5.9, 4.11, 3.58, 4.15 and 3.95%, respectively. Among the migrant fish from the floodplain, the distribution by size groups <5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45-50, and >50 cm was also similar: 12.96, 17.81, 15.15, 15.42, 12.11, 7.15, 4.77, 3.98, 3.5, 4, and 3.15%, respectively.
4 Discussion
Understanding the ecology of large river systems is most correct in considering a single complex of the riverbed and floodplain, and the temperature and phases of the hydrological regime (floods, high water and low water) are important environmental factors that determine the ecology of the complex as a whole (Tockner et al., 2000; Górski et al., 2012; Abrial et al., 2019). Natural river floodplains are complex systems consisting of a network of aquatic and terrestrial ecosystems (Stoltefaut et al., 2024). The floodplain is connected with the riverbed complex by both permanent and intermittent watercourses (Penha et al., 2017), ensuring the migration and dispersal of aquatic organisms during high water and floods, as well as from the floodplain to the main channel during low water.
Freshwater fish use specific biotopes that are available at different times of the year, and migrate between them depending on hydrological conditions (Brönmark et al., 2014). In our case, we examine the migration cycle of common fish in the Lower Irtysh from the main watercourse (the Irtysh River) to the floodplain via a small tributary during their spring seasonal migration. Modern research shows (Górski et al., 2013; Brönmark et al., 2014), that fish from almost all freshwater habitats undergo migratory movements on various spatial and temporal scales. Most freshwater fish migrations are seasonal. Individual fish movements occur in response to seasonal shifts in environmental factors that influence food availability, predation risk, the search for optimal conditions and environmental characteristics for reproduction, and the growth and survival of juveniles (Górski et al., 2013; Brönmark et al., 2014).
The intensive migration of fish population from the Irtysh riverbed to its floodplain during the flood period and the presence of a hydrological connection occurs due to the fact that floodplain areas have a greater range of water temperatures (Tockner et al., 2000), providing the temperature optimum that the fish population strives for (Amat-Trigo et al., 2023). Along with the increase in water temperature in the floodplain-channel complex, the metabolism of carp fish intensifies [Slavík et al., 2024]. This, in turn, increases their swimming and migration abilities. It’s worth noting that carp are the most abundant fish in our study area.
Mass migration of fish into the floodplain ensures not only reproduction, but also their fattening, since the food supply is intensively developing in the floodplain areas: zoobenthos, zooperiphyton and zooplankton (Górski et al., 2013; Yanygina et al., 2025). Their mass development of food objects contributes to their high availability, which ensures higher growth rates of fish (Slavík et al., 2024).
The developed floodplain-channel complex of the Lower Irtysh, with its biotopic contrasts in both the channel and the floodplain, allows for the analysis of patterns and triggers of fish migrations. In spring, spring-spawning fish species (perch, carp, and pike) migrate to the Varpak River (Chemagin, 2020).
Walsh et al. (2012) showed that the behavior of perch representatives in terms of intensive movement up and downstream in the spring period is associated with their spawning behavior and changes in water temperature. The directed migration of carp from a flat river to a flooded floodplain indicates the influence of a hydrological factor (Jones and Stuart, 2009). A study of carp and perch migration has shown that some of the main environmental factors influencing the intensity of migration of these fish during the spawning period are changes in water level, temperature and illumination (Hohausová et al., 2003). Another study found that a number of abiotic factors, including temperature, influence seasonal changes in the structure and spatial distribution of fish populations in the Yangtze River complex (Chen et al., 2022).
As in our previously published works (Chemagin, 2019; Chemagin and Schletterer, 2023), as a result of this study, it was established that the main factors influencing the dynamics of seasonal migration of common fish species in the floodplain-channel complex of the Lower Irtysh in the direction of the floodplain, regardless of the size of the watercourse, are temperature and water level. Changes in temperature and water level are known factors initiating fish migration (Howell et al., 2010; Nunn et al., 2010; Nagayama et al., 2022), which is also confirmed by our data. The general trend and quantitative indicators of fish migration in the Varpak River in spring reflect the high migratory activity of fish in the spring after the rivers open up, accompanied by a simultaneous rise in temperature and water level. Fish migration from the main river to the flooded floodplain is determined by both the temperature and the hydrological regime of the watercourse.
In addition, according to the data of the present study, as well as a previously conducted one, it was shown (Chemagin and Schletterer, 2023) that the increase in the intensity of seasonal migration of fish into the floodplain, depending on the daily regime of the river, is graphically distributed in antiphase with daily fluctuations in illumination. Many researchers have noted differences in abundance and biomass, structure of fish communities, as well as periods of peak activity in different fish during daylight and dark hours (Djemali et al., 2008; Baumgartner et al., 2008; Zhang et al., 2020). In the Lower Irtysh, the maximum increase in fish activity (migration intensity) is observed in both small and large watercourses during the twilight-night period preceding morning twilight (5 hours 30 minutes) (Chemagin and Schletterer, 2023). For many fish species in temperate climates, there is an increase in migration activity during the twilight-night period (Pavlov et al., 2007; Nunn et al., 2010; Pavlov et al., 2007). A slight increase in fish activity is recorded in the period preceding evening twilight. Between 12:00 and 15:00, when peak illumination of 69 x 103 lux is recorded, the fish migration rate (ind./hour) reaches its minimum. The average proportion of migrants (%/h) during this period is 3.01–3.23% in a small tributary and 3.0–4.94% in a large tributary of the total number of fish registered in 1 day [Chemagin and Schletterer, 2023]. Nunn et al. [2010] have shown that the daily intensity of fish migration in lowland river-tributary systems is determined primarily by the diurnal variation in illumination. Researchers note [Knott et al., 2020] that during the dark hours of the day in lowland European rivers, the migration intensity (ind./h) can exceed the daily rate by more than twice that of the daytime. In our case, the daily movements of fish begin to intensify in the twilight-night period preceding the morning twilight, and also, to a lesser extent, in the period preceding the evening twilight.
The maximum intensity of fish migration during the twilight-night period is considered (Haraldstad et al., 2017) as an adaptation to prevent predation. That is, the illumination factor during the migration period plays a significant role in the relationship of fish in the “predator-prey” system, since seasonal migration is spawning for some groups of fish, and feeding for others. Moreover, the main predatory fish species migrating to the Lower Irtysh floodplain for spawning and feeding – perch (Perca fluviatilis), pike-perch (Sander lucioperca) and pike (Esox lucius) – are visually oriented predators.
In the seasonal aspect, the main determining factors of fish migration are temperature in combination with changes in water level, which in turn is comparable with the results of our studies in the Konda River (Chemagin and Schletterer, 2023). During the spring flood period, a dynamic pattern of these factors is observed. Their influence on migration intensity is approximately 67%. Long-term hydroacoustic studies on the Yalong River (China) also showed (Zhang et al., 2020) that upstream fish migration behavior is determined by the combined effects of river flow, water temperature, and time of day, with the largest number of migrant fish also recorded at night. For carp fish, which are the most common species in our study area, the maximum intensity of migration is noted (Hohausová et al., 2003; Kärgenberg et al., 2022) during the twilight-night period. At the same time, it has been shown that fish that have light-sensitive organs associated with the central nervous system have a complex circadian mechanism, which ensures their physiological adaptation to periodically changing environmental conditions – lighting, temperature and other factors, which, to a certain extent, ensures their success in escaping from predators, finding food and choosing optimal conditions for migration (Zhdanova and Reebs, 2005).
The identified pattern of initially migrating fish with larger body sizes and a constant trend of decreasing their share among migrant fish shows (Heim et al., 2016) that fish with a higher amount of lipids migrate earlier than fish in a more depleted state, waiting for more optimal conditions for migration. We also believe that the explanation for the earlier migration of the largest individuals can, to some extent, be supplemented by a migratory model of fish behavior in seasonal freshwater environments based on trade-offs. The trade-off between predation risk and optimal growth conditions initiates and maintains differential migration, namely, larger prey fish individuals are less accessible to predators (Dermond et al., 2019), despite being more common in smaller floodplain areas. This allows them to take advantage of early migration opportunities, as during this period the choice for smaller fish shifts to avoiding the risk of predation. Only when higher temperatures are reached and larger areas of temporary floodplain reservoirs formed by flooding the floodplain are created, the choice for small fish shifts to optimal growth, outweighing the risk of predation (Dermond et al., 2019). With a more flooded floodplain, the frequency of encounters between predator and prey decreases, while the probability of survival, on the contrary, increases (Torgersen, 2007).
The similar intensity of fish movement upstream and downstream, using carp as an example, is explained by the plasticity of natural responses to varying environmental factors. As a result, the timing of migration to spawning grounds is optimized depending on environmental factors and the individual fish’s experience. In the absence of suitable temperatures, fish individuals perform forward and reverse migrations within the watercourse (Benitez and Ovidio, 2017). Thus, synchronous bursts of migratory activity of fish both upstream and downstream may be a manifestation of a certain fish tactic: “testing” their strength for further migration into the floodplain and making a decision to wait for more favorable conditions for seasonal migration. Both temperature and water level appear to be stimuli for decision-making. The latter factor triggers changes in turbidity and water chemistry. Turbidity, in turn, also triggers the migration of various fish species (Sudo et al., 2017; Šmejkal et al., 2023).
At the same time, the peculiarity of simultaneous movements of fish downstream and upstream, during seasonal migration, was previously recorded by us in a large tributary (Chemagin and Schletterer, 2023). This feature was also noted during the downstream migration of young fish from the floodplain during the summer-autumn low water period (Mochek et al., 2015). During these periods, carp also dominated among the fish (Mochek et al., 2015; Chemagin and Schletterer, 2023). The manifestation of this feature is recorded with the preservation of general trends in fish movements: during seasonal migration – into the floodplain, during downstream migration – from the floodplain. During downstream migration, fish movements upstream are an adaptive strategy for the most complete use of food resources in the productive floodplain while simultaneously reducing the availability of habitat in the floodplain areas (Mochek et al., 2015).
Synchronous daily surges and attenuations of migratory activity of fish as they move into and out of the floodplain reflect the complex mechanism of seasonal migration of fish, primarily carp, which are dominant in the area of our research.
In a large tributary of the Lower Irtysh, this feature is evident both along the banks as well as in its central parts (Chemagin and Schletterer, 2023). The complex mechanism of fish migration in floodplain-channel complexes is the result of an adaptive behavioral response, which is aimed at the most efficient use of floodplain resources, internal energy reserves, taking into account the avoidance of visually oriented predatory fish species.
Thus, the spatial and temporal distribution of migratory fish in the floodplain is determined by a combination of abiotic factors. Lighting has a significant and statistically confirmed influence on a daily basis, while temperature and water level have a seasonal impact. Seasonal fish migration in the floodplain-channel complex has a complex, synchronous, bidirectional, and differential nature. Certain characteristics have been noted for this migration: spatio-temporal size separation of migrant fish, as well as the synchronicity of migratory activity of fish upstream and downstream in seasonal and daily aspects.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical review and approval was not required for the study involving animals in accordance with the local legislation and institutional requirements because we did not handle animals. We used hydroacoustics, which is a non-invasive survey method, thus the survey did not cause any harm to or involve direct intervention with the animals.
Author contributions
AC: Conceptualization, Investigation, Methodology, Writing – original draft, Data curation, Formal analysis, Resources, Visualization. EP: Investigation, Methodology, Visualization, Writing – original draft. MS: Conceptualization, Investigation, Methodology, Writing – original draft.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was carried out with the support of the fundamental scientific research topic “Patterns of distribution and migration of fish in lotic and limnic water bodies of the Ob-Irtysh basin” (Ministry of Science and Higher Education of the Russian Federation, № 1022040700418-3-1.6.21).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffwsc.2026.1836466/full#supplementary-material
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Summary
Keywords
fish migration, floodplain, hydroacoustics, illumination, water temperature, anadromous fish
Citation
Chemagin AA, Popova EI and Schletterer M (2026) Seasonal migration of fish into the floodplain of a large plain river: temperature, water level and light are among the driving forces of migration. Front. Freshw. Sci. 4:1836466. doi: 10.3389/ffwsc.2026.1836466
Received
22 March 2026
Revised
16 May 2026
Accepted
31 May 2026
Published
06 July 2026
Volume
4 - 2026
Edited by
Maxim Vinarski, Saint-Petersburg State University, Russia
Reviewed by
Evgeny Esin, Severtsov Institute of Ecology and Evolution (RAS), Russia
Shikang Liu, Sichuan University, China
Updates
Copyright
© 2026 Chemagin, Popova and Schletterer.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Martin Schletterer, martin.schletterer@boku.ac.at
†ORCID: Andrey A. Chemagin, orcid.org/0000-0003-2515-4244; Elena I. Popova, orcid.org/0000-0002-6874-8133; Martin Schletterer, orcid.org/0000-0002-1896-3329
Disclaimer
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