In light of the controversy about the pros and cons of hydropower, a variety of monitoring programs have been initiated to examine the effects of conventional and innovative hydropower technologies on fish passage. Examinations of seasonal and diurnal patterns of fish passage [1
], assessments of the acceptance of different corridors for downstream passage [2
], as well as analyses of external and internal injury patterns after passage [4
], all depend on stow-net catches of fish at hydropower facilities. Stow-net-based monitoring at hydropower turbine outlets in small- to medium-sized rivers is considered a gold standard to investigate turbine-related fish injury and bypass efficiency compared to camera- or sonar-based technologies [7
A well-established approach for fish monitoring at hydropower plants includes the use of a full stow net, which forms the guiding unit, in combination with a fyke net, which is the catch unit. Emptying intervals vary widely, but recent studies point at the necessity of retrieving fish from these nets after rather short (i.e., hourly) intervals to avoid increased mortality and additional injuries [11
Information on catch efficiency in stow-fyke nets used for hydropower monitoring are scarce [7
]. Besides the extreme hydraulic conditions at turbine outlets, which challenge the technical installation of stow-fyke nets, the catch efficiency of those can be highly dependent on unique onsite conditions, which determine the technical constraints for installation of the net. Additionally, fish behavior may play a major role in catch efficiency. Both fish behavior as well as net performance are most likely influenced by size, shape and material of the catch device, the amount and composition of floating debris, fish biomass, fish species and size, as well as exposure time [11
]. Pander et al. [11
] studied catch efficiency and fish damage in stow nets combined with different catch units. This study revealed that some stow-fyke nets had a catch efficiency of only 73%. More specifically, catch efficiency of the species Salmo trutta
was 55.2% after 1 h and 26.2% after 12 h exposure time. Understanding the reasons of lower than expected catch rates in relation to fish behavior is an important prerequisite in interpreting data from such monitoring, yet remains largely unconsidered.
Most scientific studies on catch efficiency focus on commercial fish catching methods and include analyses of mesh size selectivity of cod ends in trawl gear, and in gillnets. These studies usually use size selection models (mostly logit models) to predict at which body size and shape a fish will be retained by the gear [12
]. Thus, morphological features of diverse fish species (dead condition) and the change in mesh shape and size during fishing have been investigated to understand under which conditions a fish would fit through the mesh [17
]. Studies focusing on cod end selectivity are more common in marine science [13
], while most studies from freshwater focus on gillnets and are conducted in lentic waters [16
It is important to differentiate between the selectivity of the guiding unit and the actual catch unit in a stow-fyke net. Although small mesh sizes provide a higher catch efficiency, they increase the risk of net damage during high loads of debris or under unfavorable hydraulic conditions. Hence, the net is separated into different sections with different mesh sizes. The largest mesh sizes are located at the entrance, i.e., the front of the stow net and then gradually become smaller towards the tail, with the fyke net having the smallest mesh size. Hence, one would assume that fish would more likely escape or enter the net in the guiding unit, which is characterized by visible fiber and larger mesh sizes compared to the catch unit and thus easier to access by the fish. However, the risk of fish swimming through the meshes is typically ignored in studies on fish passage monitoring. The fish are thought to be disorientated after turbine passage and get quickly carried away by the current to net zones with smaller mesh sizes where they no longer fit through [19
While selection models only allow conclusions on the probability of fish swimming through the net mesh, different kinds of fish behavior have been observed during several stow-net experiments (e.g., [20
]). These provide evidence that fish display a diverse set of behaviors that lead to them not being caught. For example, individual fish were observed escaping but also entering through the larger meshes of the net (“sneaker fish”) or dwelling at a certain spot of the net that is not the catch unit (“dwellers”). This behavior remained unconsidered in fish-monitoring practices at hydropower plants to date. Yet, if such behavior frequently occurs, it is possible that it results in a bias towards underrepresentation of fish in the catch that passed the turbine with no or little injuries that are in turn more likely to escape from the net. The opposite, an underestimation of turbine effects in the total catch, can occur in the case of healthy fish entering the net from outside. These examples illustrate the importance of understanding fish behavior in stow nets and its role in catch efficiency and turbine related fish injury estimations.
In this study, the fish behavior and catch efficiency of stow-fyke nets were examined in relation to a fish’s natural morphology (“fall-through experiment”), its willingness to approach and swim through fish nets (“net-perception experiment”) and its overall movement profile in stow nets during standardized sampling conditions at a hydropower facility (“stow-fyke-net experiment”). Brown trout of different sizes were used as model species. It was hypothesized that: (i) brown trout interact with the net on a voluntarily basis by trying to swim through; (ii) larger brown trout differ in their behavior from smaller brown trout corresponding to their greater ability for sustained and burst swimming; and (iii) catch efficiency is reduced when individuals show specific behavioral patterns, which prevent the fish from getting trapped in the fyke net (e.g., sneaking).
Findings of this study confirm that fish of a size between 3 and 23 cm swim through the meshes of standardized stow nets to either enter or leave the net during regular fish monitoring of hydropower plants. As expected, the frequency and amount of fish swimming through the net thereby most strongly depend on the fish size as well as the mesh size. In addition, we were able to record other behavioral patterns, namely dwelling and commuting, which can lead to fish not reaching or escaping the catch unit of the fyke net.
The observed behavior of fish escaping or entering the fishing gear as well as the occurrence of dwellers and commuters can bias the catch outcome and its interpretation. This includes interpretations concerning number and species of fish moving downstream as well as the assessment of fish mortality and injuries resulting from turbines. For example, if the catch includes fish that have entered the net from outside (sneaker), this may lead to an overrepresentation of unaffected fish, resulting in an underestimation of turbine effects. In turn, the presence of dwellers and commuters may lead to an overestimation of turbine effects, as the more agile and potentially less impacted fish are not caught in the catch unit.
Sneakers, fish that fit and swim through the net mesh of the stow net, occurred in the size range of 3–23 cm. While the stow-net mesh width gradually decreases, the net selectivity will naturally increase. Hence, fish < 7 cm (5.3–8.9 cm) fit through all meshes of the stow net. Fish < 18 cm fit through the 30 and 20 mm meshes, which account for ca. 50% of the total net area. Brown trout in the size range of 3–23 cm represent a large proportion or sometimes even 100% of the size distribution of natural brown trout populations [24
], making these findings highly relevant for fish populations in the wild. This holds also true for many other common stream fish not investigated here, e.g., European grayling (Thymallus thymallus
), European nase (Chondrostoma nasus
), European minnow (Phoxinus phoxinus
), common roach (Rutilus rutilus
), common dace (Squalius cephalus
) and gudgeon (Gobio gobio
]. Knott et al. [1
] found that the average total length of downstream-moving fish was 10 ± 6 cm (mean ± SD) based on 39 fish species recorded in central European catchments. The chance that fish of those size ranges are physically able to escape the mesh during regular hydropower fish monitoring is consequently very high.
Sneaking behavior is not necessarily linked to an escape reflex. In our study, the fish started to actively explore their environment after 10 min of acclimatization time. Some would swim through the meshes of the net or try to (often aggressively and repeatedly) by putting their snout through the mesh or by biting the net. The latter mainly occurred in fish > 11 cm, which were too large to actually fit through the distinctive mesh sizes. Some fish were observed to force themselves through the net by turning their body to the side (Supplementary Material Video S4
). This behavior also explains why the logistic-regression model of the fall-through experiment, excluding fish behavior, underestimated the predicted size of fish fitting through the different mesh sizes in the net-perception experiment. In contrast to the fish that were dropped onto the net, free ranging fish can use their body flexibility and take advantage of the net flexibility to some extent. Length is a good indicator for net selectivity, but to obtain a more realistic prediction, it is recommended to use a specific measuring technique where both the largest circumference and the strongest bone structures are considered [17
]. However, this procedure is time-consuming and requires special equipment.
Adjusting the mesh sizes to smaller meshes seems to be a logic consequence to minimize sneaking behavior. Unfortunately, the extreme hydraulic conditions at turbine outlets determine the technical constraints for installation and design of stow-fyke nets. Hence, the size, shape and material of the catch device are manufactured to withstand a particular flow rate, pressure and amount of attached and floating debris [11
]. Thus, the possibility for net adjustments such as further decreasing mesh sizes are very limited.
In addition, other behavior such as dwelling or commuting, which also contribute to a reduced recapture rate, must be considered. The results of the fyke-net experiment demonstrated that commuting behavior can cause 6% of fish to be missed in the catch after 1 h. The exact effect of dwelling on the catch needs further investigation. Brown trout are strong swimmers that sustain swimming at flow velocities of 0.7 m·s−1
for at least 200 min [44
]. Hence, brown trout could spend several hours in the stow net where flow velocities were on average 0.4 m·s−1
) without getting exhausted and drifting into the fyke net. Similar to the net perception experiment, fish needed some time to acclimate before they became active. Hence, the longer the fish are exposed to a novel situation, the higher the chance that they start to explore their environment and to show sneaking, dwelling or commuting behavior. Our experiment also suggests an influence of daytime with an increase in the catch efficiency from morning hours to noon and evening, which may be explained by the diurnal activity patterns of fish that are well-known from other studies on fish passage [1
] (and references therein).
There are indications that personality of fish plays a role in explorative and reactive behavior [45
]. We observed that, on average, 16% of the fish actively explored and tried to swim through the mesh (bold individuals), while others would stay motionless at one spot (described as “freezing” in [45
]) or explore cautiously, not attempting to swim through the mesh. This held true for trying to swim though the net (net-perception experiment) as well as for escaping the fyke-net throat (fyke-net experiment). For example, a typical escape in the fyke net was characterized by the fish slowly entering the fyke-net throat, until it had passed the half-way point, to then burst swim upstream into the stow net. Besides these individuals with explorative nature, some fish (mainly in age class 1+ and 2+) in the net-perception experiment became active and tried to swim or swam through the net when a conspecific came to close or attacked them (with snout or flank).
Besides personality, other factors such as adaptation to specific flow conditions and health status can influence fish behavior as well [25
]. As rheophilic fish, brown trout belong to the strong swimmers and are adapted to high flow rates. However, critical swimming speeds and the ability to burst swim are species-specific and can further be influenced by health status of the fish [51
]. For example, fish infested by parasites show a significant reduction in their critical swimming speed [53
], probably also affecting their behavior inside of a net. Consequently, the variation in fish behavior under natural settings is likely to even be greater than the one observed within our standardized experiments with one single species and specimens of the same origin and uniform good health condition.
It is assumed that during fish monitoring at hydropower plants, dead or injured fish will passively drift with the current and are then caught in the fyke net. However, depending on the flow current it is possible that dead fish do not reach the catch unit and are missed. Thus, further investigations on how fish condition influences the catch efficiency are highly recommended.
Certainly, there is a variety of factors that challenge the implementation of strategies to deal with the monitoring of bias caused by fish behavior. However, great progress has been made in recent years in minimizing influential effects. One example is the standardization of fish monitoring, where, besides the monitoring of the natural occurring fish fauna, standardized fish releases help to set turbine effects into relation to number of fish passing the hydropower plant. Hereby, selected species representing different body and size classes are released in known numbers [2
]. Further improvements were made by considering the health status of these fish following a standardized protocol including condition, parasite load and injuries [6
As supported by our findings, innovative technologies like camera- or sonar-based systems could be used to further improve fish monitoring at hydropower plants and set observed behavioral effects and catch efficiency into relation [7
]. However, some specific limitations need to be considered when selecting one of these methods and when interpreting the results. Possible limitations include turbidity conditions, imprecise recording of fish length and underrepresenting fish of size < 100–150 mm (both systems), light conditions (camera-based) or false detection inferences and signal masking (sonar-based) [7
]. Sonar-based systems can be operated day and night and can cover areas of 5–15 m (e.g., ARIS Explorer [57
]). However, in highly turbulent waters such as at turbine outlets, the acoustic signal is scattered by the gas bubbles forming in the water column and targets get readily masked behind the acoustic bubble cloud [56
]. Although video cameras are limited to a visual range of 1.5–2 m in clear freshwater, the quality of the recorded objects is very high and species identification is possible. Automated video analysis systems are currently under development [60
] and could reduce the workload significantly, making it a cheap and fast method for additional monitoring.