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Article

How Much Hatchery-Reared Brown Trout Move in a Large, Deep Subalpine Lake? An Acoustic Telemetry Study

1
Water Research Institute–National Research Council, Corso Tonolli 50, 28922 Verbania-Pallanza, Italy
2
Ufficio della caccia e della pesca, Repubblica e Cantone Ticino, Via F. Zorzi 13, 6500 Bellinzona, Switzerland
*
Author to whom correspondence should be addressed.
Environments 2024, 11(11), 245; https://doi.org/10.3390/environments11110245
Submission received: 12 September 2024 / Revised: 30 October 2024 / Accepted: 4 November 2024 / Published: 6 November 2024

Abstract

:
Fish movement into large, deep lakes has been rarely investigated due to the complexity and extent of such ecosystems. Among the different monitoring methods available, acoustic telemetry enables the study of the spatial ecology and behavior of aquatic organisms in lentic environments. In this study, the movement of 69 hatchery-reared adult brown trout (size 43–61 cm) marked with acoustic transmitters was monitored in the large and deep subalpine Lake Lugano (Switzerland and Italy). Trout were tracked for six consecutive months by seven acoustic receivers (March–August 2022), positioned in a non-overlapping array. Trout movement was reconstructed using R packages specific for acoustic telemetry (actel and RSP), which also allowed us to translate tracking information into utilization distribution (UD) areas for each fish. The effects of different environmental variables (rainfall, water discharge of the two main tributaries of Lake Lugano, atmospheric pressure, cloud coverage, and moon phases) on trout movement were tested, but none of these variables seemed to significantly correlate with fish movement. After release, most of the tagged fish exhibited reiterative movements during the initial month, with some maintaining this behavior throughout the entire study period. This spatial behavior can be particularly evident in hatchery-reared fish due to their aggressive and bold attitude. The association of these behavioral traits, shaped by domestication, could expose hatchery-reared fish to high risks and post-release mortality in the wild. Indeed, within a few months after the release, most of the tagged fish were no longer detected by the acoustic receivers. In addition, 26% of the total tagged fish were caught by recreational or professional fishermen.

1. Introduction

Animal telemetry technologies have increasingly advanced in recent years, providing new and easy-access ways to study animal movement over large areas [1,2,3]. Acoustic tracking, or telemetry, is one the most used methods to study animal spatial ecology over a range of habitats and taxa, especially in aquatic environments [3,4,5,6]. This method has been extensively used in long-term studies to track fish movement across various scales, from small to large [7], to quantify fish-stock structure [8], migratory routes [9,10], and habitat utilization in response to abiotic and biotic stimuli [11].
Although acoustic telemetry is a well-established approach for these types of studies, its application over large spatial areas requires great investments both in economic (e.g., a high number of receivers) and operative terms (e.g., installation and maintenance of the receiver array) [1]. Consequently, most of the telemetry studies carried out so far have focused either on small study areas or on specific portions of large aquatic environments, such as bays or fjords [12,13,14,15]. Only a few studies have investigated fish movement in deep large lakes, due to the complexity and extension of such ecosystems [1,16]. However, studying the movements of fish species that are relevant either for conservation and/or human activities (e.g., commercial and recreational fishing) in these habitats can unveil important information about their behavior, ecological needs, and environmental constraints [17,18].
Fish stocking is still a common practice in nearly all freshwater ecosystems [19,20]; thus, studying the movement of stocked fish in the wild could help in evaluating the effectiveness of stocking programs, especially in relation to fishery management and conservation efforts [18]. As a result, rearing and stocking programs could be adjusted to apply proper management strategies to meet fishery demands, as well as to reach conservation goals [18,21,22].
Among freshwater fish species, brown trout (Salmo trutta Linnaeus, 1758) play a significant role in sustaining both professional and recreational fisheries globally [23,24], representing a valuable ecological and cultural resource, as well as a remarkable economic income [25]. The economic importance of brown trout has led to a massive introduction of this species in both lentic and lotic environments, aimed at enhancing fisheries’ yields and/or restoring wild populations [26,27,28,29]. Brown trout are stocked at different life stages (eggs, larvae, alevins, adults) depending on local needs and hatchery production capacity [30,31]. Hatchery-reared trout are commonly released at adult stages, since they increase the number of catchable fish both in the short and long term [31]. Releasing hatchery-reared trout at adult stages has the advantage of reducing predation pressure exerted by birds and other piscivorous fish, since larger sizes are usually associated with lower predation risks [32]. However, fish reared in tanks are subjected to domestication processes, which can be more pronounced as the time spent in captivity increases [33,34].
In general, domestication can affect both fish behavior and phenotype [35,36,37,38]. For example, domestication may select sedentary individuals that invest more in growth rather than in movement, which is also a consequence of the low-flow-rate conditions in hatchery tanks [39,40,41]. Furthermore, space limitation and over-density conditions of the rearing environment heavily reduce domesticated fish swimming performances [37,42]. Conversely, some authors stress that domestication can also lead to the selection of more aggressive, explorative, and bolder individuals [32,36,43], showing an association of behavioral traits known as “behavioral syndrome”, i.e., a suite of behavioral traits that tend to co-vary together in different situations [44,45,46].
Predator-free rearing environments can further trigger the selection of bolder individuals that tend to show relaxed antipredator behaviors once released into a wild environment, reducing antipredator vigilance and promoting exploratory attitudes [32,36,47,48,49]. Moreover, larger and older hatchery-reared fish have elevated energetic demands compared to their younger counterparts, which increases their willingness to take exploratory risks in search of feeding opportunities [32,47,50]. Risks associated with predation are reduced for larger individuals [32,51,52]; however, the trade-off between benefits associated with exploration and the high energetic costs of traveling long distances can significantly influence the post-release survival of adult hatchery fish [47]. Another downside of the distinct aggressiveness and boldness of hatchery-reared fish is their higher vulnerability to hook and line capture [53,54]. In fact, exploratory behavior can expose fish to a greater risk of capture by different fishing gears such as gillnets used by professional fishermen [55,56].
In this study, we used acoustic telemetry to monitor the movement of adult brown trout stocked in a large and deep perialpine lake (Lake Lugano). This study was conducted over six consecutive months (March–August 2022) using a fixed station array. The specific objectives were to (i) assess the fate of hatchery-reared adult brown trout, regularly stocked in Lake Lugano to support fishery needs, by monitoring their movement within the lake, (ii) identify possible differences in behavioral strategies based on fish spatial distribution, and (iii) determine whether space use was influenced by environmental factors such as rainfall, water discharge of the main tributaries in the study area, atmospheric pressure, cloud coverage, and moon phases.
The movement and fate of hatchery-reared adult trout were expected to be significantly influenced by domestication effects, which negatively affect adaptation success in a wild environment [57]. The results presented in this work may provide valuable insights for evaluating the effectiveness of stocking efforts in these types of environments.

2. Materials and Methods

2.1. Study Area

Lake Lugano is a natural lake located at the southern fringe of the Central Alps, on the border between southern Switzerland and northern Italy (Figure 1).
Lake Lugano has a catchment area of 565.6 km2 and is characterized by a humid temperate climate known as an “Insubric climate”. Winters are mild (January average daily temperature = 3.3 °C), summers are hot and humid (July average daily temperature = 22.1 °C), and yearly precipitation is high (annual average = 1559 mm). Because of the mild winters, the lake remains ice-free throughout the winter [58].
Lake Lugano is fed by numerous small mountain streams, among which the most important ones are Vedeggio, Cassarate, and Cuccio, while its main outlet is Tresa River, which connects Lake Lugano to Lake Maggiore.
It is a relatively large lake with a surface area of 48.9 km2, 63% of which belongs to Switzerland and 37% to Italy. It has an average width of roughly 1 km and a maximum width of about 3 km at the bay of Lugano. It lies at 271 m above sea level and its maximum depth is 288 m [59].
The lake is divided into 3 main basins: the northern basin and the southern basin, divided by a natural moraine, and the small Ponte Tresa basin [60].
The northern basin is the largest (27.5 km2) and the deepest (max. depth = 288 m, medium depth = 171 m), as well as the basin with the longest renewal time (12.3 years). It has a meromictic regime due to the process of eutrophication that started in the second half of the last century [61]. During summer, the thermal stratification epilimnetic water temperature reaches 16–18 °C, while hypolimnetic water is steady throughout the year with a temperature of 6 °C [61,62].
The southern basin is smaller (21.4 km2) and shallower (max. depth = 95 m, medium depth = 55 m), and has a short residence time (1.4 years). This basin has a monomictic regime; therefore, it turns over at the end of every winter [61,62] when temperatures reach 5–6 °C along the water column. Thermal stratification starts in April and extends until the end of the year. During summer, the epilimnetic water temperature exceeds 24 °C (with a peak of 27 °C recorded in 2018), while hypolimnetic water is more stable due to stratification, with a temperature of 6.5 °C.
Ponte Tresa basin is the smallest (1.1 km2) and the shallowest (max. depth = 50 m, medium depth = 33 m), and has a renewal time of 0.4 years. It is characterized by a monomictic regime, with complete mixing around February and March when temperatures reach 5–6 °C along the water column. Epilimnetic temperature can reach 24 °C during summer, while hypolimnetic temperature is relatively more stable with temperatures that range from 4.5 °C to 6.5 °C [63].

2.2. Study Species and Tagging Procedure

The studied species is brown trout Salmo trutta Linnaeus, 1758. Brown trout is a Salmonid native of the Atlantic, North, White, and Baltic Sea Basins, from Spain to Chesha Bay (Russia). It is also found in Iceland, Great Britain, and the upper Danube and Volga drainages. Due to its importance in aquaculture and sport fisheries, brown trout have been introduced outside their natural range in Europe, North America, southern and eastern Africa, Pakistan, India, Nepal, Japan, New Zealand, and Australia [23,64].
It typically inhabits cold streams, rivers, and lakes, with many known anadromous populations. The species exhibits tolerance to a wide range of environmental conditions, including temperature (0.0–26.0 °C depending on life cycle stages; [65]), salinity (0–35‰), pH (5.0–9.0), and dissolved oxygen (2.5–8.6 mg L−1; [66]), contributing to its complex ecology. This eco-physiological plasticity allows brown trout to develop different eco-phenotypes, such as anadromous, lacustrine, and resident forms [23].
Lake Lugano is home to a few small and dispersed populations of wild brown trout. The main predators of this species in the lake are pike (Esox lucius), pikeperch (Sander lucioperca), and wels catfish (Silurus glanis) [67].
This study was conducted on stocked fish coming from the hatchery located in Maglio di Colla, Lugano (Switzerland). Trout were reared under natural light conditions in open-air tanks (volume: 25 to 50 m3) and fed with commercial pellets. Water temperature in the hatchery tanks remained stable for most of the year at around 9–10 °C. Due to tag sizes, only fish larger than 40 cm (4 and 5 years old) were selected for the surgical implantation of acoustic tags, following the tag-to-body weight ratio of 2% [68,69].
Seventy-one fish were selected, measured (total length; mean ± SD = 540 ± 37 mm, min = 430, max = 610), and weighed (total weight; mean ± SD = 2101 ± 407 g, min = 1191, max = 2979). After these steps, fish were implanted with internal acoustic transmitters (LOTEK MM-R-11-28, approx. 202 days battery life with 60 s pulse rate, frequency = 69 kHz, weight = 10.0 g, size (ØxL) = 12 × 60 mm; MM-R-11-45, approx. 1573-day battery life with 60 s pulse rate, frequency = 69 kHz, weight = 14.0 g, size (ØxL) = 12 × 72 mm). The tag implantation procedure followed the methods used in previous acoustic telemetry studies [70,71,72]. Every fish (on an empty stomach) was anesthetized with eugenol solution (0.2 mg L−1) until loss of equilibrium was reached. Fish were placed in a V-shaped tagging support and the transmitter was surgically implanted into the body cavity through a small incision (<15 mm) made with a cutfix stainless scalpel #10. The incision was closed with a sterile synthetic absorbable surgical suture.
Additionally, T-Bar tags (red color) were externally implanted in the dorsal part of the fish (slightly below the dorsal fin) for immediate identification in case of recapture.
All fish were kept in the hatchery for ten days after tagging to monitor their recovery. Only two fish died after the monitory period. The remaining fish were randomly divided into four groups, each containing a different number of individuals, and released at four different locations in Lake Lugano (6 at Brusimpiano, 25 at Capo San Martino, 27 at Agno, 11 at Ponte Tresa; Figure 1).

2.3. Acoustic Receiver Network

Seven acoustic telemetry receivers (LOTEK WHS 6000L—69 kHz, weight in air = 1 Kg, size (ØxL) = 60 × 370 mm, 4 × D-cell lithium batteries with a life of 6–10 months) were positioned at seven different locations in Lake Lugano (Figure 1). Receivers were deployed in a non-overlapping array at strategic locations corresponding to lake stretches and obligated passages (Ponte Tresa, Diga Nord and Diga Sud) or at inlet mouths (Vedeggio, Magliasina, Maroggia, Cassarate) (Figure 1).
All receivers were placed at a depth of 15 m to minimize the effects of thermal stratification on the detection range [3]. Six receivers were mounted on the mooring chains of pre-existing buoys, whereas the seventh receiver, located in Ponte Tresa channel, was fixed on a pole positioned on a submerged shoal. All receivers were attached to supports with cable ties, mounted upright with the transducer oriented towards the surface [73].
The coordinates of every receiver station were recorded with a GPS (Global Positioning System) receiver.
The acoustic receiver network timestamps the signal from tagged fish upon its arrival at the hydrophone, thereby recording the presence or absence of the fish in that specific area. The detection range of the receivers was not tested in Lake Lugano. According to the manufacturer’s information, in a deep lake environment, the Lotek WHS 6000 receiver has a maximum detection range of approximately 2000 m and an average detection range (with 50% detection efficiency) of about 500 m. The average detection range is shown in Figure 1.

2.4. Data Processing

Receivers were positioned in the lake on 15 March 2022 and retrieved on 27 August 2022 with the assistance of scuba divers from the Cantonal Police of Ticino.
Data from the receivers were downloaded using the WHS Host software (WHS Host x64 Build, v1.5.2870.1, Lotek Wireless Inc. 2012, 115 Pony Dr, Newmarket, ON L3Y 7B5, Canada) at the end of the study period. Raw detection data were saved in .csv format and then manually filtered to select only detections belonging to tagged animals. All the following steps in data processing were carried out with R version 4.2.2 using the R package actel [74]. Duplicated detections and detections that did not fall within the deployment period (March–August 2022) were removed.
Potential detections created by acoustic tag collisions (interference between multiple overlapping transmissions) [1,75] or ambient noise [76] were tested. If an animal was detected by two different receivers consecutively and the time interval between detections was shorter than the time needed for the fish to swim between the two locations (based on the maximum swimming speed reported for salmonids), the second detection was removed. A reference value of 10 body lengths per second was used for maximum trout swimming speed, corresponding to 5.4 ± 0.4 m·s−1 for the trout in this study, as reported by Castro-Santos et al. [77]. Data filtering was performed manually by inspecting temporary files created by the actel function explore(). In addition, receivers placed in the proximity of obligated passages were used to validate fish movement among stations.

2.5. Post-Release Behavior

To assess whether hatchery-reared trout remained in the proximity of the release site or moved to other areas of the lake during the first month after release, the number of detections registered by each receiver for each individual was analyzed. The number of detections timestamped by the receiver located near the release site was compared with those recorded by all other receivers. For fish released at Brusimpiano (Figure 1), the nearest receiver considered was at Magliasina, while for the other release sites, the closest receiver was used (Agno->Vedeggio station; Capo San Martino->Cassarate station; Ponte Tresa->Ponte Tresa Station).
In addition to evaluating fish proximity to the release site, we also analyzed those exhibiting reiterative movement during the first month. Reiterative movement was defined as a fish moving between receivers at least three times during this period, even if it involved only two receivers (see Figure 2 for an example). Fish that only moved between two receivers twice, for example, making a single back-and-forth trip without further movements, were not considered to exhibit movement reiteration.

2.6. Utilization Distribution (UD)

Utilization distribution (UD) represents the likelihood for a studied organism to occupy a specific area of space at any given time; in this study, it can be seen as a measure of the relative time spent by a fish in a particular area of the lake during the study period [78].
Daily UDs were calculated using a dynamic Brownian Bridge Movement Model (dynBBMM). The BBMM estimates the movement paths of animals and their habitat use [79]. The dynamic aspect of dynBBMM accounts for varying speeds over time, providing a more accurate representation of fish movement [80].
The dynBBMM was calculated using the function dynBBMM() (default values) within the R package RSP [81]. The area of less intense use, corresponding to the extended range area (95% UD), was measured using the function getAreas().

2.7. Cluster Analysis

Daily UDs were used to group individuals with similar spatial behaviors into clusters. The cluster analysis was performed using the R package ts2net [82], which transforms one or multiple time series into networks. A network represents time series by nodes, which are clustered together based on similarity.
The distance between every pair of time series was calculated using a cross-correlation function and stored in a distance matrix D. This distance matrix was then transformed into an adjacency matrix, A, from which a nearest-neighbor network (Є-NN network) was constructed.
The cluster threshold was set at a 0.5 percentile, based on both the movement profiles generated by actel and the observation that lower threshold values resulted in an excessive number of groups. This percentile choice helped balance the number of groups identified and the clarity of the clustering results, ensuring that distinct movement patterns were captured without over-fragmentation.
To assess whether the groups identified by the cluster analysis were significantly different, a Kruskal–Wallis test followed by a Dunn’s test was conducted across the different groups. These tests were selected due to the non-normal distribution of daily UDs.

2.8. Influence of Size on Cluster Groups

To assess whether fish size influenced the groups defined by cluster analysis, a Kruskal–Wallis test was performed. This test was selected due to the non-normal distribution of both the UD and size data.

2.9. Correlation Between Space Use and Abiotic Factors

The correlation between each individual’s daily UD and daily values of various abiotic factors potentially affecting fish behavior was tested using the Pearson correlation coefficient. The following factors were tested: rainfall, atmospheric pressure (Lugano Weather Station), cloud cover, lunar phases (interpolated values from multiple stations around Lugano), and water discharge of Vedeggio and Cassarate streams (the two main tributaries in the area covered by receivers). Environmental data were downloaded from the Environmental Observatory of Switzerland [83] and from Visual Crossing: Weather Data and Weather API [84].

3. Results

3.1. Sample Description

Following data filtering, the dataset comprised 852,992 detections across the seven receivers, although there was significant variability in the number of detections among individuals. For instance, 19 fish (28%) were recorded with fewer than 1000 detections.
Only 18 fish (26%) were detected throughout the entire study duration. Of the 69 tagged fish, 32 (46%) disappeared from the detection range of the receivers a few months after release, 18 fish (26%) were captured by recreational/professional anglers (with 15 of these being harvested), 4 fish (6%) were never recorded, and 1 fish that stationed in a single location for the entire study period was presumed dead and excluded from the dataset.

3.2. Post-Release Behavior

The percentage of detections timestamped by the nearest receiver for each fish is reported in Table 1. During the first month, 36 out of the 63 fish analyzed by actel had more than 50% of their total detections near the release site (two fish were registered a few times only after the first month).
During the first month after the release, 38 fish (55%) exhibited reiterative movements (see an example of reiterative movement in Figure 2).
Of these 38 fish, 13 were subsequently harvested by recreational or professional anglers (2 harvested fish did not display reiterative movements).

3.3. Utilization Distribution (UD) and Cluster Analysis

Only the 18 fish detected throughout the entire study duration were considered for the following utilization distribution (UD) and cluster analysis. Although the cessation of detections for some fish might be due to fish moving to areas not covered by receivers, including only those detected throughout the study period ensures a standardized dataset and accurate spatial behavior interpretation. Biometric data, daily UDs, and movement profiles for these fish are provided in Table 1, Supplementary Table S1, and Supplementary Figure S1, respectively.
Seven distinct patterns, corresponding to different spatial behavioral groups, were identified by the cluster analysis. However, only two of them (Figure 3) formed groups composed of more than one individual (one cluster made of seven fish, one made of six fish). The remaining five individuals did not fit into any of these two behavioral groups (daily UDs for these fish are reported in Supplementary Figure S2).
Fish in Group (a) displayed a pattern of increasing space usage at the beginning of the study period (lasting approximately two to three months), followed by a period of limited movement and minimal area usage until the end of the study. The duration of the stationary period varied among fish in this group (movement profiles are shown in Supplementary Figure S1). Fish in this group were released at different sites.
Fish in Group (b) exhibited large movements during two main periods, utilizing a large portion of the Lake Lugano area: one at the beginning and another at the end of the study. A period of reduced mobility, lasting about a month, corresponded to the start of summer.
Four out of six fish in this group were released at the same site located near the Cassarate station (Capo San Martino; see Figure 1). Three of these fish returned to the Cassarate station after the movement period.
Fish not clustering within the previous two groups exhibited unique movement patterns. For example, one fish (tag 12543) continuously moved throughout the entire study period, traveling among six receivers multiple times (see Supplementary Figure S1).
Groups identified by the cluster analysis were compared using the Kruskal–Wallis test, which indicated a significant difference between the groups (p-value < 2.2 × 10−16). Consequently, a post hoc test (Dunn’s test) was conducted to determine which specific groups differed from each other (Table 2).
In particular, Group (a) was not statistically different from Group (f) (tag 12543), which exhibited similar spatial behavior to fish within Group (a) but had a much larger movement rate. Group (b) showed no statistical difference from Group (e) (tag 12540), which also displayed two distinct movement windows at the beginning and end of the study period. Group (c) did not statistically differ from Groups (b), (e), and (f), demonstrating a mixed movement pattern that was not clearly identified by the cluster analysis (Table 2).

3.4. Influence of Size on Cluster Groups

Following the Kruskal–Wallis test, no statistically significant differences in fish size were found across the different groups (or clusters) of fish (p-value = 0.4435). The box plot displaying the fish size distribution among the different groups is shown in Figure 4.

3.5. Correlation Between Space Use and Abiotic Factors

The influence of abiotic factors on fish space use was assessed using the Pearson correlation test.
Daily rainfall values recorded at the Lugano Weather Station (mean ± SD = 3.01 ± 7.6 mm; calculated as the mean of daily rainfall averages) showed a weak (r = 0.16) but significant correlation (p < 0.05) with space use for only one individual (Table 3). Atmospheric pressure (measured at 301 m above sea level) remained relatively stable (mean ± SD = 981.0 ± 6.2 hPa) during the study period. Four individuals showed a weak (r < 0.4) but significant correlation with atmospheric pressure variations (Table 3).
Cloud cover data (percentage of the sky covered by clouds) were highly variable (mean ± SD = 36.6 ± 25.4%) during the study period and showed a weak (r < 0.3) but significant correlation with the space use in only two individuals (Table 3).
The lunar cycle (values representing the portion of lighted moon) allowed us to predict the change in space use of three individuals only (r < 0.3; Table 3).
Cassarate stream, the main tributary of the northern basin, had a limited water discharge (mean ± SD = 0.71 ± 0.48 m3 s−1) during the study period compared to the previous 5 years (mean ± SD = 2.02 ± 0.30 m3 s−1, calculated as the average of daily water discharge mean). Three individuals exhibited a weak (r < 0.2) but significant correlation with the water discharge variation in this stream (Table 3).
In contrast, Vedeggio stream, the main tributary of the southern basin, experienced considerable fluctuations in water discharge (mean ± SD = 1.39 ± 1.20 m3 s−1) during the study period. Only one individual showed a weak (r = 0.21) but significant correlation between space use and Vedeggio stream flow variation (Table 3).

4. Discussion

In this study, the movement of adult brown trout stocked in a large alpine lake was monitored over six months using a non-overlapping acoustic telemetry array.
Most of the tagged fish disappeared from the detection range of acoustic receivers within a few months after the release, with many of them being harvested by recreational or professional fishermen.
Of the 63 fish registered during the first month, 37 (57%) tended to remain close to the release site area, as indicated by the high percentage of detections recorded by the receivers near the release site. This result has been observed in other studies that have reported a high release site fidelity for brown trout, both in tagged wild individuals [72,85] and hatchery-reared ones [86] (in Watz et al. [86], release site fidelity was primarily influenced by a structurally enriched rearing environment). Other fish species have also shown a strong release site fidelity [72,87,88]. However, this type of analysis may be biased either by fish that died near the release site or by variations in the detection range of the receivers. Nonetheless, the latter point may be less relevant in this study due to the proximity between the receivers and the release site in most cases.
Overall, 55% of the tagged fish performed reiterative movements during the first month after the release in Lake Lugano, moving between receivers multiple times, with some showing a tendency to return to their release site.
The analysis carried out on the 18 selected fish enabled us to identify two major fish groups corresponding to different post-release behavior and usage of Lake Lugano. Most of these fish exhibited extensive movements after the release, followed by a period of reduced mobility. After this phase, some fish resumed reiterative movements between receiver locations (as indicated by the UD analysis and the movement profiles obtained from actel), while others remained inactive for the rest of the study. A few fish exhibited continuous movement throughout the entire study period, with some displaying spatial behaviors similar to those of the two main cluster groups, (a) and (b).
Extensive movement behaviors of stocked fish have already been observed by other studies [89,90,91], which suggested the key role of the hatchery environment in shaping fish behavior, leading to a greater dispersion in the wild [92,93].
Moreover, within a fish population, individuals often exhibit different personalities characterized by various behavioral traits that tend to co-vary. This association between behavioral traits is referred to as “behavioral syndrome” [44,46]. According to the behavioral syndrome hypothesis, fish with a fast exploration strategy are typically more aggressive and bolder in a novel environment [44,46]. These behavioral traits may be non-adaptive under natural conditions, resulting in greater post-release energetic costs [94] and increased exposure to predation [95,96] and fishing [55,90]. Indeed, Alòs et al. [97] demonstrated that all fishing techniques, including the least efficient one (i.e., fixed spot fishing), consistently exert negative selection pressure on activity-like behaviors such as exploratory, aggressive, and bold behaviors. Among others, gears used by professional anglers (i.e., gillnets) represent a major threat to populations maintained by stocking practices due to the high probability for a highly mobile fish to encounter nets, leading to increased harvest rates. This elevated risk was also evident in this study, where 13 out of 15 fish harvested by recreational or professional anglers exhibited repeated movements between receivers during the first month after release (see Figure 5 for an example of a harvested fish).
Exploratory and bold behaviors, coupled with the reduced plasticity and swimming performances of hatchery-reared fish [98,99,100,101], could result in a high post-release mortality.
Assessing the mortality of studied animals in acoustic telemetry experiments is important to avoid biased data interpretations, especially when they can influence management and conservation decisions [102].
Following the fish mortality assessment criteria used in other telemetry studies applicable to this work (i.e., cease in detections, stationary or variable horizontal space use, and harvest information and/or direct observations of death by anglers and commercial fishers [102,103]), 51 out of 69 tagged fish potentially died after release, indicating a 74% post-release mortality rate. This number could be even higher, as most fish of Group (a) remained stationary for several months after the first movement window. This mortality rate is comparable to or even higher than the one observed in other telemetry studies on hatchery-reared fish [104,105] and may be related to their poor adaptation to the lake environment, as only two fish died due to surgical implantation after the monitory period. However, tagging procedures may have different implications once a fish is released in a wild environment, such as altering its natural behavior, increasing stress levels, and potentially affecting swimming performance and energy expenditure [106,107]. Acoustic tagging can influence post-release behavior and survival rates, even several weeks after release, even though in situ research regarding the extent of these effects is limited [108]. Nevertheless, it must be acknowledged that the application of mortality assessment criteria in this study is made difficult by the large distance that separates receivers, which may cause the mortality assessment of fish that moved to areas not covered by receivers (cease in detection criterion), or that may have stopped performing extensive movements, remaining within a single receiver detection range (stationary or variable horizontal space use criterion). Another factor that could lead to erroneous mortality estimations is, for instance, the variability in detection efficiency, which can be caused by background noise, transmitter signal collisions, seasonal or daily variation in water physico-chemical properties, and signal interferences with the substrate [109,110,111]. In this study, the variation in detection range was not tested, instead using the average detection range based on manufacturer statements and similar literature works [5,112]. Using the average detection range of 500 m (see Figure 1), it is evident that a large portion of Lake Lugano is not covered by acoustic receivers, limiting further assumptions on the cease in detections.
Additionally, dead animals or expelled tags (from predation events or tag rejection [113]) can be covered by sediment within a few days, significantly or totally reducing detection efficiency [114].
In addition, assessing predation events on tagged fish is a challenging task since the tag may be retained in the living predator after consumption, introducing a potential “predation bias” [115]. Therefore, further mortality investigations (i.e., transmitters with heart rate/depth sensors), or the extension of the monitoring activity on Lake Lugano, will be crucial to accurately quantify the post-release survival of hatchery-reared brown trout in this lake.
Environmental factors such as atmospheric conditions [116,117], lunar cycles [11], and hydrological changes [118,119,120] are also important drivers of fish movement and activity inside aquatic ecosystems. In addition, other studies pointed out that the direction, timing, and extent of fish movements may be influenced by a variety of other variables including turbidity [121], chemical factors such as pH [122], oxygen levels [123], and thermal stratification [124,125]. In this study, the environmental factors correlated with fish movement included rainfall, water discharge from the two main tributaries of Lake Lugano, atmospheric pressure, cloud coverage, and moon phases. None of these factors were able to reliably predict changes in trout space use, with only sparse and weak significant correlations (r < 0.4, p < 0.05) observed in a few individual cases. However, it must be pointed out that the lack of information regarding fish vertical distribution (transmitters used were not equipped with depth sensors) limits further assumptions about the relation between fish space use and environmental changes.

5. Conclusions

This study provides insights into the post-release behavior of adult stocked brown trout in a large, deep lake over six months. The results underline how adult stocked brown trout tend to perform reiterative movements following their introduction.
This behavior could be linked to domestication, which can have a remarkable influence on the behavioral strategies adopted by the hatchery fish once released in the wild. The reduced plasticity of hatchery-reared fish induces individuals to reiterative movements, potentially leading to higher risks and mortality due to increased energy consumption, predation and fishing. High post-release mortality seems to occur in hatchery-reared brown trout, as suggested by harvest reports and cease in detections. However, mortality rate estimations should be further investigated due to the limited number of receivers deployed, especially for a large area like Lake Lugano.
Given the small outcomes derived from the stocking practices observed in this study, these practices should be carefully evaluated before committing significant financial resources and efforts. The potentially high mortality rates of stocked fish, along with elevated harvest rates, could undermine the effectiveness of stocking efforts, particularly when aimed at conservation objectives.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/environments11110245/s1, Figure S1. Movement profiles of the 18 analyzed fish obtained by actel. The x-axis shows the study period. St. 1: Cassarate; St. 2: DigaNord; St. 3: DigaSud; St. 4: Maroggia; St. 5: Vedeggio; St. 6: Ponte Tresa; St. 7: Magliasina; Figure S2. Behavioral clusters built transforming daily UDs (extended range) time series into networks. The five individuals that did not cluster with the two main behavioral groups are shown separately; Table S1. daily UDs (km2) of the 18 analyzed fish.

Author Contributions

S.B.: project design, data collection, data analysis, writing, editing. L.M.: data collection, data analysis, writing, editing. C.M.: project design, data collection, editing. T.P.: project design, editing. P.V.: project design, writing, editing. All authors have read and agreed to the published version of the manuscript.

Funding

Funding for this study was provided by the project Interreg ITA-CH SHARESALMO (Grant no. 599030).

Institutional Review Board Statement

Scientific research for this paper was conducted under the project “Conservazione dei salmonidi autoctoni”, N° TI-27-2020 permit released by Dipartimento della sanità e della socialità Esperimenti sugli animali Autorizzazione/decisione delle autorità.

Data Availability Statement

Data and analysis code will be made available upon request.

Acknowledgments

LIFE15 NAT/IT000823 IdroLIFE and LIFE21-NAT-IT-PREDATOR Projects. We thank the office of hunting and fishing of Canton Ticino and the Cantonal Police of Ticino, Switzerland, for the data collection, field work, and revisions. We thank Cesare Puzzi and Andrea Tersigni (G.R.A.I.A. srl) for the support during the tagging and monitoring activities. We thank Milan Riha for the insightful comments regarding data analysis.

Conflicts of Interest

The authors declare no competing interests.

References

  1. Heupel, M.R.; Semmens, J.M.; Hobday, A.J. Automated acoustic tracking of aquatic animals: Scales, design and deployment of listening station arrays. Mar. Freshw. Res. 2006, 57, 113. [Google Scholar] [CrossRef]
  2. Kessel, S.T.; Cooke, S.J.; Heupel, M.R.; Hussey, N.E.; Simpfendorfer, C.A.; Vagle, S.; Fisk, A.T. A review of detection range testing in aquatic passive acoustic telemetry studies. Rev. Fish Biol. Fish. 2014, 24, 199–218. [Google Scholar] [CrossRef]
  3. Binder, T.R.; Holbrook, C.M.; Hayden, T.A.; Krueger, C.C. Spatial and temporal variation in positioning probability of acoustic telemetry arrays: Fine-scale variability and complex interactions. Anim. Biotelem. 2016, 4, 4. [Google Scholar] [CrossRef]
  4. Espinoza, M.; Farrugia, T.J.; Webber, D.M.; Smith, F.; Lowe, C.G. Testing a new acoustic telemetry technique to quantify long-term, fine-scale movements of aquatic animals. Fish Res. 2011, 108, 364–371. [Google Scholar] [CrossRef]
  5. How, J.R.; De Lestang, S. Acoustic tracking: Issues affecting design, analysis and interpretation of data from movement studies. Mar. Freshw. Res. 2012, 63, 312–324. [Google Scholar] [CrossRef]
  6. Hellström, G.; Klaminder, J.; Jonsson, M.; Fick, J.; Brodin, T. Upscaling behavioural studies to the field using acoustic telemetry. Aquat. Toxicol. 2016, 170, 384–389. [Google Scholar] [CrossRef]
  7. Espinoza, M.; Farrugia, T.J.; Lowe, C.G. Habitat use, movements and site fidelity of the gray smooth-hound shark (Mustelus californicus Gill 1863) in a newly restored southern California estuary. J. Exp. Mar. Biol. Ecol. 2011, 401, 63–74. [Google Scholar] [CrossRef]
  8. Lédée, E.J.I.; Heupel, M.R.; Taylor, M.D.; Harcourt, R.G.; Jaine, F.R.A.; Huveneers, C.; Udyawer, V.; Campbell, H.A.; Babcock, R.C.; Hoenner, X.; et al. Continental-scale acoustic telemetry and network analysis reveal new insights into stock structure. Fish Fish. 2021, 22, 987–1005. [Google Scholar] [CrossRef]
  9. Binder, T.R.; Farha, S.A.; Thompson, H.T.; Holbrook, C.M.; Bergstedt, R.A.; Riley, S.C.; Bronte, C.R.; He, J.; Krueger, C.C. Fine-scale acoustic telemetry reveals unexpected lake trout, Salvelinus namaycush, spawning habitats in northern Lake Huron, North America. Ecol. Freshw. Fish 2018, 27, 594–605. [Google Scholar] [CrossRef]
  10. Verhelst, P.; Buysse, D.; Reubens, J.; Pauwels, I.; Aelterman, B.; Van Hoey, S.; Goethals, P.; Coeck, J.; Moens, T.; Mouton, A. Downstream migration of European eel (Anguilla anguilla L.) in an anthropogenically regulated freshwater system: Implications for management. Fish Res. 2018, 199, 252–262. [Google Scholar] [CrossRef]
  11. Bašić, T.; Aislabie, L.; Ives, M.; Fronkova, L.; Piper, A.; Walker, A. Spatial and temporal behavioural patterns of the European eel Anguilla anguilla in a lacustrine environment. Aquat. Sci. 2019, 81, 73. [Google Scholar] [CrossRef]
  12. Pinnix, W.D.; Nelson, P.A.; Stutzer, G.; Wright, K.A. Residence time and habitat use of coho salmon in Humboldt Bay, California: An acoustic telemetry study. Environ. Biol. Fishes 2013, 96, 315–323. [Google Scholar] [CrossRef]
  13. Eldøy, S.H.; Davidsen, J.G.; Thorstad, E.B.; Whoriskey, F.G.; Aarestrup, K.; Næsje, T.F.; Rønning, L.; Sjursen, A.D.; Rikardsen, A.H.; Arnekleiv, J.V. Marine depth use of sea trout Salmo trutta in fjord areas of central Norway. J. Fish Biol. 2017, 91, 1268–1283. [Google Scholar] [CrossRef] [PubMed]
  14. Lewandoski, S.A.; Bishop, M.A.; McKinzie, M.K.; William, P. Evaluating Pacific cod migratory behavior and site fidelity in a fjord environment using acoustic. Can. J. Fish. Aquat. Sci. 2018, 75, 2084–2095. [Google Scholar] [CrossRef]
  15. Ellis, R.D.; Flaherty-Walia, K.E.; Collins, A.B.; Bickford, J.W.; Boucek, R.; Walters Burnsed, S.L.; Lowerre-Barbieri, S.K. Acoustic telemetry array evolution: From species- and project-specific designs to large-scale, multispecies, cooperative networks. Fish Res. 2019, 209, 186–195. [Google Scholar] [CrossRef]
  16. Abecasis, D.; Steckenreuter, A.; Reubens, J.; Aarestrup, K.; Alós, J.; Badalamenti, F.; Bajona, L.; Boylan, P.; Deneudt, K.; Greenberg, L.; et al. A review of acoustic telemetry in Europe and the need for a regional aquatic telemetry network. Anim. Biotelem. 2018, 6, 12. [Google Scholar] [CrossRef]
  17. Lucas, M.C.; Baras, E. Methods for studying spatial behaviour of freshwater fishes in the natural environment. Fish Fish. 2000, 1, 283–316. [Google Scholar] [CrossRef]
  18. Landsman, S.J.; Nguyen, V.M.; Gutowsky, L.F.G.; Gobin, J.; Cook, K.V.; Binder, T.R.; Lower, N.; McLaughlin, R.L.; Cooke, S.J. Fish movement and migration studies in the Laurentian Great Lakes: Research trends and knowledge gaps. J. Great Lakes Res. 2011, 37, 365–379. [Google Scholar] [CrossRef]
  19. Aprahamian, M.W.; Smith, K.M.; McGinnity, P.; McKelvey, S.; Taylor, J. Restocking of salmonids-opportunities and limitations. Fish. Res. 2003, 62, 211–227. [Google Scholar] [CrossRef]
  20. Riepe, C.; Fujitani, M.; Cucherousset, J.; Pagel, T.; Buoro, M.; Santoul, F.; Lassus, R.; Arlinghaus, R. What determines the behavioral intention of local-level fisheries managers to alter fish stocking practices in freshwater recreational fisheries of two European countries? Fish Res. 2017, 194, 173–187. [Google Scholar] [CrossRef]
  21. Cornelius, F.C.; Muth, K.M.; Kenyon, R. Lake Trout Rehabilitation in Lake Erie: A Case History. J. Great Lakes Res. 1995, 21, 65–82. [Google Scholar] [CrossRef]
  22. Cooke, S.J. Biotelemetry and biologging in endangered species research and animal conservation: Relevance to regional, national, and IUCN Red List threat assessments. Endanger Species Res. 2008, 4, 165–185. [Google Scholar] [CrossRef]
  23. Bagliniere, L. Introduction: The brown trout (Salmo trutta L.)—Its origin, distribution and economic and scientific significance. In Biology and Ecology of the Brown and Sea Trout; Springer: London, UK, 1999; pp. 1–12. [Google Scholar]
  24. Lobón-Cerviá, J.; Sanz, N. (Eds.) Brown Trout: Biology, Ecology and Management; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2018. [Google Scholar]
  25. Guinand, B.; Oral, M.; Tougard, C. Brown trout phylogenetics: A persistent mirage towards (too) many species. J. Fish Biol. 2021, 99, 298–307. [Google Scholar] [CrossRef] [PubMed]
  26. Von Lindern, E.; Mosler, H.J. Insights into fisheries management practices: Using the theory of planned behavior to explain fish stocking among a sample of Swiss anglers. PLoS ONE 2014, 9, e115360. [Google Scholar] [CrossRef] [PubMed]
  27. Casalinuovo, M.A.; Alonso, M.F.; Macchi, P.J.; Kuroda, J.A. Brown trout in Argentina: History, interactions and perspectives. In Brown Trout: Life History, Ecology and Management; Wiley: Hoboken, NJ, USA, 2017; pp. 599–621. [Google Scholar] [CrossRef]
  28. Christophe, M. Certificate of Advanced Studies (CAS) Poissons d’eau douce d’Europe-Ecologie et Gestion Caractérisation Génétique des Truites de Rivière du Canton du Tessin. Travail de Certificat de Christophe Molina. 2019. Available online: https://www4.ti.ch/fileadmin/DT/temi/pesca/rapporti/CAS__ChristopheMolina_Caracterisation_genetique_des_truites_de_riviere_du_Canton_Tessin.pdf (accessed on 29 May 2023).
  29. Polgar, G.; Iaia, M.; Righi, T.; Volta, P. The Italian Alpine and Subalpine trouts: Taxonomy, Evolution, and Conservation. Biology 2022, 11, 576. [Google Scholar] [CrossRef]
  30. Jorgensen, J.; Berg, S. Stocking experiments with 0+ and 1+ trout parr, Salmo trutta L., of wild and hatchery origin: 2. Post-stocking movements. J. Fish Biol. 1991, 39, 171–180. [Google Scholar] [CrossRef]
  31. Baer, J.; Blasel, K.; Diekmann, M. Benefits of repeated stocking with adult, hatchery-reared brown trout, Salmo trutta, to recreational fisheries? Fish Manag. Ecol. 2007, 14, 51–59. [Google Scholar] [CrossRef]
  32. Sundström, L.F.; Petersson, E.; Höjesjö, J.; Johnsson, J.I.; Järvi, T. Hatchery selection promotes boldness in newly hatched brown trout (Salmo trutta): Implications for dominance. Behav. Ecol. 2004, 15, 192–198. [Google Scholar] [CrossRef]
  33. Johnsson, J.I.; Höjesjö, J.; Fleming, I.A. Behavioural and heart rate responses to predation risk in wild and domesticated Atlantic salmon. Can. J. Fish. Aquat. Sci. 2001, 58, 788–794. [Google Scholar] [CrossRef]
  34. Hansen, M.M. Estimating the long-term effects of stocking domesticated trout into wild brown trout (Salmo trutta) populations: An approach using microsatellite DNA analysis of historical and contemporary samples. Mol. Ecol. 2002, 11, 1003–1015. [Google Scholar] [CrossRef]
  35. Petersson, E.; Järvi, T.; Steffner, N.G.; Ragnarsson, B. The effect of domestication on some life history traits of sea trout and Atlantic salmon. J. Fish Biol. 1996, 48, 776–791. [Google Scholar] [CrossRef]
  36. Huntingford, F.A. Implications of domestication and rearing conditions for the behaviour of cultivated fishes. J. Fish Biol. 2004, 65, 122–142. [Google Scholar] [CrossRef]
  37. Pasquet, A. Effects of domestication on fish behaviour. In Animal Domestication; IntechOpen: Rijeka, Croatia, 2019. [Google Scholar] [CrossRef]
  38. Milla, S.; Pasquet, A.; El Mohajer, L.; Fontaine, P. How domestication alters fish phenotypes. Rev. Aquac. 2021, 13, 388–405. [Google Scholar] [CrossRef]
  39. Duthie, G.G. Observations of poor swimming performance among hatchery-reared rainbow trout, Salmo gairdneri. Environ. Biol. Fishes 1987, 18, 309–311. [Google Scholar] [CrossRef]
  40. Reinbold, D.; Thorgaard, G.H.; Carter, P.A. Reduced swimming performance and increased growth in domesticated rainbow trout, Oncorhynchus mykiss. Can. J. Fish. Aquat. Sci. 2009, 66, 1025–1032. [Google Scholar] [CrossRef]
  41. Blouin, M.S.; Wrey, M.C.; Bollmann, S.R.; Skaar, J.C.; Twibell, R.G.; Fuentes, C. Offspring of first-generation hatchery steelhead trout (Oncorhynchus mykiss) grow faster in the hatchery than offspring of wild fish, but survive worse in the wild: Possible mechanisms for inadvertent domestication and fitness loss in hatchery salmon. PLoS ONE 2021, 16, e0257407. [Google Scholar] [CrossRef]
  42. Ojanguren, A.F.; Brana, F. Effects of size and morphology on swimming performance in juvenile brown trout (Salmo trutta L.). Ecol. Freshw. Fish 2003, 12, 241–246. [Google Scholar] [CrossRef]
  43. Huntingford, F.; Adams, C. Behavioural syndromes in farmed fish: Implications for production and welfare. Behaviour 2005, 142, 1207–1221. [Google Scholar] [CrossRef]
  44. Sih, A.; Bell, A.; Johnson, J.C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 2004, 19, 372–378. [Google Scholar] [CrossRef]
  45. Conrad, J.L.; Weinersmith, K.L.; Brodin, T.; Saltz, J.B.; Sih, A. Behavioural syndromes in fishes: A review with implications for ecology and fisheries management. J. Fish Biol. 2011, 78, 395–435. [Google Scholar] [CrossRef]
  46. Spiegel, O.; Leu, S.T.; Bull, C.M.; Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 2017, 20, 3–18. [Google Scholar] [CrossRef]
  47. Johnsson, J.; Petersson, E.; Björnsson, B.; Järvi, T. Domestication and growth hormone alter antipredator behaviour and growth patterns in juvenile brown trout (Salmo trutta). Can. J. Fish. Aquat. Sci. 1996, 53, 1546–1554. [Google Scholar] [CrossRef]
  48. Einum, S.; Fleming, I.A. Genetic divergence and interactions in the wild among native, farmed and hybrid Atlantic salmon. J. Fish Biol. 1997, 50, 634–651. [Google Scholar] [CrossRef]
  49. Alvarez, D.; Nicieza, A.G. Predator avoidance behaviour in wild and hatchery-reared brown trout: The role of experience and domestication. J. Fish Biol. 2003, 63, 1565–1577. [Google Scholar] [CrossRef]
  50. Abrahams, M.V.; Sutterlin, A. The foraging and antipredator behaviour of growth-enhanced transgenic Atlantic salmon. Anim. Behav. 1999, 58, 933–942. [Google Scholar] [CrossRef]
  51. Godin, J.G. Evading predators. In Behavioral Ecology of Teleost Fishes; Oxford University Press: Oxford, UK, 1997; pp. 191–236. [Google Scholar]
  52. Öhlund, G. Ecological and Evolutionary Effects of Predation in Environmental Gradients. Ph.D. Thesis, Umeå Universitet, Umeå, Sweden, 2012. [Google Scholar]
  53. Sutter, D.A.H.; Suski, C.D.; Philipp, D.P.; Klefoth, T.; Wahl, D.H.; Kersten, P.; Cooke, S.J.; Arlinghaus, R. Recreational fishing selectively captures individuals with the highest fitness potential. Proc. Natl. Acad. Sci. USA 2012, 109, 20960–20965. [Google Scholar] [CrossRef]
  54. Keiling, T.D.; Louison, M.J.; Suski, C.D. Big, hungry fish get the lure: Size and food availability determine capture over boldness and exploratory behaviors. Fish Res. 2020, 227, 105554. [Google Scholar] [CrossRef]
  55. Biro, P.A.; Stamps, J.A. Are animal personality traits linked to life-history productivity? Trends Ecol. Evol. 2008, 23, 361–368. [Google Scholar] [CrossRef]
  56. Monk, C. Mining the Behavioural Reality of Fish-Fisher Interactions to Understand Vulnerability to Hook-and-Line Fishing. Ph.D. Thesis, Humboldt-Universität zu Berlin, Berlin, Germany, 2019. Available online: https://edoc.hu-berlin.de/handle/18452/20565 (accessed on 26 May 2023).
  57. Brown, C.; Laland, K. Social learning and life skills training for hatchery reared fish. J. Fish Biol. 2001, 59, 471–493. [Google Scholar] [CrossRef]
  58. Franchini, F.; Lepori, F.; Bruder, A. Improving estimates of primary production in lakes: A test and a case study from a peri-alpine lake (Lake Lugano). Inland Waters 2017, 7, 77–87. [Google Scholar] [CrossRef]
  59. Cannata, M.; Neumann, J.; Rossetto, R. Open source GIS platform for water resource modelling: FREEWAT approach in the Lugano Lake. Spat. Inf. Res. 2018, 26, 241–251. [Google Scholar] [CrossRef]
  60. Lavelli, A.; Boillat, J.-L.; De Cesare, G. Numerical 3D Modelling of the Vertical Mass Exchange Induced by Turbidity Currents in Lake Lugano (Switzerland). In Proceedings of the 5th International Conference on Hydro-Science and -Engineering (ICHE-2002) (Reference: LCH-CONF-2002-012 Note: [355]), Warsaw, Poland, September 2002; Available online: https://www.researchgate.net/publication/37445865 (accessed on 25 May 2023).
  61. Barbieri, A.; Polli, B. Description of Lake Lugano; Birkhauser Verlag: Basel, Switzerland, 1992. [Google Scholar]
  62. Lepori, F.; Bartosiewicz, M.; Simona, M.; Veronesi, M. Effects of winter weather and mixing regime on the restoration of a deep perialpine lake (Lake Lugano, Switzerland and Italy). Hydrobiologia 2018, 824, 229–242. [Google Scholar] [CrossRef]
  63. Tu, L.; Jarosch, K.A.; Schneider, T.; Grosjean, M. Phosphorus fractions in sediments and their relevance for historical lake eutrophication in the Ponte Tresa basin (Lake Lugano, Switzerland) since 1959. Sci. Total Environ. 2019, 685, 806–817. [Google Scholar] [CrossRef] [PubMed]
  64. Kottelat, M.; Freyhof, J. Handbook of European Freshwater Fishes; IUCN: Gland, Switzerland, 2007. [Google Scholar]
  65. Elliott, J.M.; Elliott, J.A. Temperature requirements of Atlantic salmon (Salmo salar), brown trout (Salmo trutta) and Arctic charr (Salvelinus alpinus): Predicting the effects of climate change. J. Fish Biol. 2010, 77, 1793–1817. [Google Scholar] [CrossRef]
  66. Molony, B. Environmental Requirements and Tolerances of Rainbow Trout (Oncorhynchus mykiss) and Brown Trout (Salmo trutta) with Special Reference to Western Australia: A Review; Department of Fisheries, Government of Western Australia, Fisheries Research Division: Perth, Australia, 2001.
  67. Lepori, F.; Capelli, C. Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy). J. Great Lakes Res. 2020, 46, 870–880. [Google Scholar] [CrossRef]
  68. Winter, J.D. Underwater biotelemetry. In Fisheries Techniques; Nielsen, L.A., Johnsen, J.D., Eds.; American Fisheries Society: Bethesda, Maryland, 1983; pp. 371–395. [Google Scholar]
  69. Jepsen, N. A brief discussion of the 2% tag/bodymass rule. In Aquatic Telemetry: Advances and Applications; FAO/COISPA: Rome, Italy, 2005; pp. 255–259. Available online: https://www.researchgate.net/publication/259573199 (accessed on 26 May 2023).
  70. Moore, A.; Russell, I.C.; Potter, E.C.E. The effects of intraperitoneally implanted dummy acoustic transmitters on the behaviour and physiology of juvenile Atlantic salmon, Salmo salar L. J. Fish Biol. 1990, 37, 713–721. [Google Scholar] [CrossRef]
  71. Bridger, C.J.; Booth, R.K. The effects of biotelemetry transmitter presence and attachment procedures on fish physiology and behavior. Rev. Fish. Sci. 2003, 11, 13–34. [Google Scholar] [CrossRef]
  72. Barry, J.; McLoone, P.; Fitzgerald, C.J.; King, J.J. The spatial ecology of brown trout (Salmo trutta) and dace (Leuciscus leuciscus) in an artificially impounded riverine habitat: Results from an acoustic telemetry study. Aquat. Sci. 2020, 82, 63. [Google Scholar] [CrossRef]
  73. Domeier, M.L. Methods for the deployment and maintenance of an acoustic tag tracking array: An example from California’s Channel Islands. Mar. Technol. Soc. J. 2005, 39, 74–80. [Google Scholar] [CrossRef]
  74. Flávio, H.; Baktoft, H. actel: Standardised analysis of acoustic telemetry data from animals moving through receiver arrays. Methods Ecol. Evol. 2021, 12, 196–203. [Google Scholar] [CrossRef]
  75. Simpfendorfer, C.A.; Huveneers, C.; Steckenreuter, A.; Tattersall, K.; Hoenner, X.; Harcourt, R.; Heupel, M.R. Ghosts in the data: False detections in VEMCO pulse position modulation acoustic telemetry monitoring equipment. Anim. Biotelem. 2015, 3, 55. [Google Scholar] [CrossRef]
  76. Crossin, G.T.; Heupel, M.R.; Holbrook, C.M.; Hussey, N.E.; Lowerre-Barbieri, S.K.; Nguyen, V.M.; Raby, G.D.; Cooke, S.J. Acoustic telemetry and fisheries management. Ecol. Appl. 2017, 27, 1031–1049. [Google Scholar] [CrossRef] [PubMed]
  77. Castro-Santos, T.; Sanz-Ronda, F.J.; Ruiz-Legazpi, J. Breaking the speed limit-comparative sprinting performance of brook trout (Salvelinus fontinalis) and brown trout (Salmo trutta). Can. J. Fish. Aquat. Sci. 2013, 70, 280–293. [Google Scholar] [CrossRef]
  78. Kie, J.G.; Matthiopoulos, J.; Fieberg, J.; Powell, R.A.; Cagnacci, F.; Mitchell, M.S.; Gaillard, J.M.; Moorcroft, P.R. The home-range concept: Are traditional estimators still relevant with modern telemetry technology? Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 2221–2231. [Google Scholar] [CrossRef] [PubMed]
  79. Horne, J.S.; Garton, E.O.; Krone, S.M.; Lewis, J.S. Analyzing animal movements using Brownian bridges. Ecology 2007, 88, 2354–2363. [Google Scholar] [CrossRef]
  80. Kranstauber, B.; Kays, R.; Lapoint, S.D.; Wikelski, M.; Safi, K. A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. J. Anim. Ecol. 2012, 81, 738–746. [Google Scholar] [CrossRef]
  81. Niella, Y.; Flávio, H.; Smoothey, A.F.; Aarestrup, K.; Taylor, M.D.; Peddemors, V.M.; Harcourt, R. Refined Shortest Paths (RSP): Incorporation of topography in space use estimation from node-based telemetry data. Methods Ecol Evol 2020, 11, 1733–1742. [Google Scholar] [CrossRef]
  82. Ferreira, L.N. From Time Series to Networks in R with the ts2net Package. arXiv 2022, arXiv:2208.09660. [Google Scholar] [CrossRef]
  83. Environmental Observatory of Switzerland. Available online: https://www.oasi.ti.ch (accessed on 20 May 2023).
  84. Visual Crossing: Weather Data and Weather API. Available online: https://www.visualcrossing.com (accessed on 22 May 2023).
  85. Tracey, S.R.; Hartmann, K.; McAllister, J.; Lyle, J.M. Home range, site fidelity and synchronous migrations of three co-occurring, morphologically distinct estuarine fish species. Sci. Total Environ. 2020, 713, 136629. [Google Scholar] [CrossRef]
  86. Watz, J.; Calles, O.; Carlsson, N.; Collin, T.; Huusko, A.; Johnsson, J.; Nilsson, P.A.; Norrgård, J.; Nyqvist, D. Wood addition in the hatchery and river environments affects post-release performance of overwintering brown trout. Freshw. Biol. 2019, 64, 71–80. [Google Scholar] [CrossRef]
  87. Bridger, C.J.; Booth, R.K.; Mckinley, R.S.; Scruton, D.A. Site fidelity and dispersal patterns of domestic triploid steelhead trout (Oncorhynchus mykiss Walbaum) released to the wild. ICES J. Mar. Sci. 2001, 58, 510–516. [Google Scholar] [CrossRef]
  88. Pursche, A.R.; Suthers, I.M.; Taylor, M.D. Post-release monitoring of site and group fidelity in acoustically tagged stocked fish. Fish. Manag. Ecol. 2013, 20, 445–453. [Google Scholar] [CrossRef]
  89. Schulz, U.; Berg, R. Movements of ultrasonically tagged brown trout (Salmo trutta L.) in Lake Constance. J. Fish Biol. 1992, 40, 909–917. [Google Scholar] [CrossRef]
  90. Härkönen, L.; Hyvärinen, P.; Paappanen, J.; Vainikka, A. Explorative behavior increases vulnerability to angling. Can. J. Fish. Aquat. Sci. 2014, 71, 1900–1909. [Google Scholar] [CrossRef]
  91. Alioravainen, N.; Prokkola, J.; Lemopoulos, A.; Härkönen, L.; Hyvärinen, P.; Vainikka, A. Post-release exploration and diel activity of hatchery, wild and crossbred strain brown trout in semi-natural streams. EcoEvoRxiv 2019. [Google Scholar] [CrossRef]
  92. Cote, J.; Clobert, J.; Brodin, T.; Fogarty, S.; Sih, A. Personality-dependent dispersal: Characterization, ontogeny and consequences for spatially structured populations. Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 4065–4076. [Google Scholar] [CrossRef]
  93. Villegas-Ríos, D.; Réale, D.; Freitas, C.; Moland, E.; Olsen, E.M. Personalities influence spatial responses to environmental fluctuations in wild fish. J. Anim. Ecol. 2018, 87, 1309–1319. [Google Scholar] [CrossRef]
  94. Taylor, M.D.; Laffan, S.W.; Fairfax, A.V.; Payne, N.L. Finding their way in the world: Using acoustic telemetry to evaluate relative movement patterns of hatchery-reared fish in the period following release. Fish. Res. 2017, 186, 538–543. [Google Scholar] [CrossRef]
  95. Rogell, B.; Dannewitz, J.; Palm, S.; Petersson, E.; Dahl, J.; Prestegaard, T.; Järvi, T.; Laurila, A. Strong divergence in trait means but not in plasticity across hatchery and wild populations of sea-run brown trout Salmo trutta. Mol. Ecol. 2012, 21, 2963–2976. [Google Scholar] [CrossRef]
  96. Hulthén, K.; Chapman, B.B.; Nilsson, P.A.; Hansson, L.A.; Skov, C.; Brodersen, J.; Vinterstare, J.; Brönmark, C. A predation cost to bold fish in the wild. Sci. Rep. 2017, 7, 1239. [Google Scholar] [CrossRef]
  97. Alós, J.; Palmer, M.; Arlinghaus, R. Consistent selection towards low activity phenotypes when catchability depends on encounters among human predators and fish. PLoS ONE 2012, 7, e48030. [Google Scholar] [CrossRef] [PubMed]
  98. Kolok, A.S. Morphological and physiological correlates with swimming performance in juvenile largemouth bass. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 1992, 263, 1042–1048. [Google Scholar] [CrossRef]
  99. Gregory, R.; Wood, C.M. Individual variation and interrelationships between swimming performance, growth rate, and feeding in juvenile rainbow trout (Oncorhynchus mykiss). Can. J. Fish. Aquat. Sci. 1998, 55, 1583–1590. [Google Scholar] [CrossRef]
  100. McDonald, D.; Milligan, C.; McFarlane, W.; Croke, S.; Currie, S.; Hooke, B.; Angus, R.; Tufts, B.; Davidson, K. Condition and performance of juvenile Atlantic salmon (Salmo salar): Effects of rearing practices on hatchery fish and comparison with wild fish. Can. J. Fish. Aquat. Sci. 1998, 55, 1208–1219. [Google Scholar] [CrossRef]
  101. Billerbeck, J.M.; Lankford, T.E.; Conover, D.O. Evolution of intrinsic growth and energy acquisition rates. I. Trade-offs with swimming performance in Menidia menidia. Evolution 2001, 55, 1863–1872. [Google Scholar] [CrossRef] [PubMed]
  102. Klinard, N.V.; Matley, J.K. Living until proven dead: Addressing mortality in acoustic telemetry research. Rev. Fish Biol. Fish. 2020, 30, 485–499. [Google Scholar] [CrossRef]
  103. Khan, J.A.; Welsh, J.Q.; Bellwood, D.R. Using passive acoustic telemetry to infer mortality events in adult herbivorous coral reef fishes. Coral Reefs 2016, 35, 411–420. [Google Scholar] [CrossRef]
  104. Thorstad, E.B.; Uglem, I.; Finstad, B.; Chittenden, C.M.; Nilsen, R.; Økland, F.; Bjørn, P.A. Stocking location and predation by marine fishes affect survival of hatchery-reared Atlantic salmon smolts. Fish Manag. Ecol. 2012, 19, 400–409. [Google Scholar] [CrossRef]
  105. Klinard, N.V.; Matley, J.K.; Halfyard, E.A.; Connerton, M.; Johnson, T.B.; Fisk, A.T. Post-stocking movement and survival of hatchery-reared bloater (Coregonus hoyi) reintroduced to Lake Ontario. Freshw. Biol. 2020, 65, 1073–1085. [Google Scholar] [CrossRef]
  106. Rechisky, E.L.; Welch, D.W. Surgical implantation of acoustic Tags: Influence of tag loss and tag-induced mortality on free-ranging and hatchery-held spring Chinook Salmon (Oncorhynchus tshawytscha) Smolts. In Tagging, Telemetry and Marking Measures for Monitoring Fish Populations: A Compendium of New and Recent Science for Use in Informing Technique and Decision Modalities; Wolf, K., O’Neal, J., Duvall, W., Eds.; Pacific Northwest Aquatic Monitoring Partnership: Duvall, WA, USA, 2010; pp. 69–94. Available online: https://www.researchgate.net/publication/305657189 (accessed on 26 May 2023).
  107. Lawrence, M.J.; Wilson, B.M.; Reid, G.K.; Hawthorn, C.; English, G.; Black, M.; Leadbeater, S.; McKindsey, C.W.; Trudel, M. The fate of intracoelomic acoustic transmitters in Atlantic salmon (Salmo salar) post-smolts and wider considerations for causal factors driving tag retention and mortality in fishes. Anim. Biotelem. 2023, 11, 40. [Google Scholar] [CrossRef]
  108. Daniels, J.; Brunsdon, E.B.; Chaput, G.; Dixon, H.J.; Labadie, H.; Carr, J.W. Quantifying the effects of post-surgery recovery time on the migration dynamics and survival rates in the wild of acoustically tagged Atlantic Salmon Salmo salar smolts. Anim. Biotelem. 2021, 9, 6. [Google Scholar] [CrossRef]
  109. Huveneers, C.; Simpfendorfer, C.A.; Kim, S.; Semmens, J.M.; Hobday, A.J.; Pederson, H.; Stieglitz, T.; Vallee, R.; Webber, D.; Heupel, M.R.; et al. The influence of environmental parameters on the performance and detection range of acoustic receivers. Methods Ecol. Evol. 2016, 7, 825–835. [Google Scholar] [CrossRef]
  110. Selby, T.H.; Hart, K.M.; Fujisaki, I.; Smith, B.J.; Pollock, C.J.; Hillis-Starr, Z.; Lundgren, I.; Oli, M.K. Can you hear me now? Range-testing a submerged passive acoustic receiver array in a Caribbean coral reef habitat. Ecol. Evol. 2016, 6, 4823–4835. [Google Scholar] [CrossRef] [PubMed]
  111. Brownscombe, J.W.; Lédée, E.J.I.; Raby, G.D.; Struthers, D.P.; Gutowsky, L.F.G.; Nguyen, V.M.; Young, N.; Stokesbury, M.J.W.; Holbrook, C.M.; Brenden, T.O.; et al. Conducting and interpreting fish telemetry studies: Considerations for researchers and resource managers. Rev. Fish Biol. Fish. 2019, 29, 369–400. [Google Scholar] [CrossRef]
  112. Loher, T.; Webster, R.A.; Carlile, D. A test of the detection range of acoustic transmitters and receivers deployed in deep waters of Southeast Alaska, USA. Anim. Biotelem. 2017, 5, 27. [Google Scholar] [CrossRef]
  113. Halfyard, E.A.; Webber, D.; Del Papa, J.; Leadley, T.; Kessel, S.T.; Colborne, S.F.; Fisk, A.T. Evaluation of an acoustic telemetry transmitter designed to identify predation events. Methods Ecol. Evol. 2017, 8, 1063–1071. [Google Scholar] [CrossRef]
  114. Watson, W.H.; Johnson, S.K.; Whitworth, C.D.; Chabot, C.C. Rhythms of locomotion and seasonal changes in activity expressed by horseshoe crabs in their natural habitat. Mar. Ecol. Prog. Ser. 2016, 542, 109–121. [Google Scholar] [CrossRef]
  115. Weinz, A.A.; Matley, J.K.; Klinard, N.V.; Fisk, A.T.; Colborne, S.F. Identification of predation events in wild fish using novel acoustic transmitters. Anim. Biotelem. 2020, 8, 28. [Google Scholar] [CrossRef]
  116. Girard, P.; Boisclair, D.; Leclerc, M. The effect of cloud cover on the development of habitat quality indices for juvenile Atlantic salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 2003, 60, 1386–1397. [Google Scholar] [CrossRef]
  117. Payne, N.L.; van der Meulen, D.E.; Gannon, R.; Semmens, J.M.; Suthers, I.M.; Gray, C.A.; Taylor, M.D. Rain reverses diel activity rhythms in an estuarine teleost. Proc. R. Soc. B Biol. Sci. 2013, 280, 20122363. [Google Scholar] [CrossRef]
  118. Jonsson, N. Influence of water flow, water temperature and light on fish migration in rivers. Nord. J. Freshw. Res. 1991, 66, 20–35. [Google Scholar]
  119. Milner, N.J.; Solomon, D.J.; Smith, G.W. The role of river flow in the migration of adult Atlantic salmon, Salmo salar, through estuaries and rivers. Fish Manag. Ecol. 2012, 19, 537–547. [Google Scholar] [CrossRef]
  120. Trépanier, S.; Rodríguez, M.A.; Magnan, P. Spawning migrations in landlocked Atlantic salmon: Time series modelling of river discharge and water temperature effects. J. Fish Biol. 1996, 48, 925–936. [Google Scholar] [CrossRef]
  121. Kulíšková, P.; Horký, P.; Slavík, O.; Jones, J.I. Factors influencing movement behaviour and home range size in ide Leuciscus idus. J. Fish Biol. 2009, 74, 1269–1279. [Google Scholar] [CrossRef] [PubMed]
  122. Gagen, C.I.; Sharpe, W.E.; Carline, R.F.; Gagen, C.J.; Sharpe, W.E.; Carline, R.F. Downstream movement and mortality of brook trout (Salvelinus fontinalis) exposed to acidic episodes in streams. Can. J. Fish. Aquat. Sci. 1994, 51, 1620–1628. [Google Scholar] [CrossRef]
  123. Plumb, J.M.; Blanchfield, P.J. Performance of temperature and dissolved oxygen criteria to predict habitat use by lake trout (Salvelinus namaycush). Can. J. Fish. Aquat. Sci. 2009, 66, 2011–2023. [Google Scholar] [CrossRef]
  124. Cline, T.J.; Bennington, V.; Kitchell, J.F. Climate change expands the spatial extent and duration of preferred thermal habitat for Lake Superior fishes. PLoS ONE 2013, 8, e62279. [Google Scholar] [CrossRef]
  125. Ivanova, S.V.; Johnson, T.B.; Metcalfe, B.; Fisk, A.T. Spatial distribution of lake trout (Salvelinus namaycush) across seasonal thermal cycles in a large lake. Freshw. Biol. 2021, 66, 615–627. [Google Scholar] [CrossRef]
Figure 1. Map of the study area. The four fish release points (1: Ponte Tresa; 2: Agno; 3: Brusimpiano; 4: Capo San Martino) and the seven acoustic receivers with their average detection range are shown.
Figure 1. Map of the study area. The four fish release points (1: Ponte Tresa; 2: Agno; 3: Brusimpiano; 4: Capo San Martino) and the seven acoustic receivers with their average detection range are shown.
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Figure 2. Example of movement profile and a fish performing reiterative movements during the first month.
Figure 2. Example of movement profile and a fish performing reiterative movements during the first month.
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Figure 3. The two groups (a,b) identified by the cluster analysis, each consisting of multiple individuals (indicated by their respective tag numbers in the rectangular boxes). To improve clarity and readability, fish of each group are shown separately.
Figure 3. The two groups (a,b) identified by the cluster analysis, each consisting of multiple individuals (indicated by their respective tag numbers in the rectangular boxes). To improve clarity and readability, fish of each group are shown separately.
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Figure 4. Size distribution (total length) of fish forming the 7 groups identified by the cluster analysis.
Figure 4. Size distribution (total length) of fish forming the 7 groups identified by the cluster analysis.
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Figure 5. On the left, a fish marked on 9 March 2022 with an acoustic transmitter and released in Lake Lugano; on the right, the same fish recaptured on 30 December 2022 (9 months later) by a recreational angler.
Figure 5. On the left, a fish marked on 9 March 2022 with an acoustic transmitter and released in Lake Lugano; on the right, the same fish recaptured on 30 December 2022 (9 months later) by a recreational angler.
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Table 1. Summary of tagged fish information, including release site, biometric parameters, and the percentage of detections near the release site (% det). CSM = Capo San Martino.
Table 1. Summary of tagged fish information, including release site, biometric parameters, and the percentage of detections near the release site (% det). CSM = Capo San Martino.
TagRelease SiteT. Length (mm)Mass (g)% detTagRelease SiteT. Length (mm)Mass (g)% det
12472Agno540225264.612545CSM530206484.5
12510CSM53018230.012546CSM5302238100.0
12511CSM550219537.212548Agno540202837.3
12513CSM540198035.112549CSM55023881.9
12514Agno520202660.012550Agno600286020.4
12515CSM5401999100.012551CSM540195467.9
12516Agno470146188.412552CSM54021938.8
12518Agno610289357.812554Agno580286760.2
12519CSM54021345.112555Agno580238240.6
12520CSM5101675100.012556Agno59026420.0
12521CSM58025655.712557CSM53018059.2
12522Agno540192191.612558Agno510183877.7
12523Agno600264889.612559CSM520174216.8
12524CSM430119142.212560CSM550235527.4
12525Agno5402070100.012561Agno510170594.0
12526Agno560243053.412562Agno5601727100.0
12527Agno570243415.812563CSM5602017100.0
12528Agno46015630.612564Agno560203663.7
12529CSM48014140.012565Agno550229197.0
12530Agno5702258100.012580PonteTresa5652300100.0
12531Agno510183098.212581PonteTresa530129062.4
12532CSM5401927100.012582PonteTresa45915004.8
12533Agno590297986.712584Brusimpiano5702300100.0
12534Agno540186265.112588Brusimpiano5552200100.0
12535CSM53023618.612592Brusimpiano5302100100.0
12537Agno580240726.312593Brusimpiano560225098.6
12538CSM57025501.812594PonteTresa5102000nd
12539CSM5802618100.012596Brusimpiano4711500nd
12540CSM550210252.612597Brusimpiano54521000.0
12541Agno580247320.812600PonteTresa438128775.2
12542CSM49014909.012627PonteTresa485143488.2
12543CSM56023560.412629PonteTresa5602387100.0
12544Agno550230969.5
Table 2. Results of the Dunn’s test carried out on the groups identified by the cluster analysis. p-values indicating statistical significance (p < 0.05) are marked with an asterisk (*).
Table 2. Results of the Dunn’s test carried out on the groups identified by the cluster analysis. p-values indicating statistical significance (p < 0.05) are marked with an asterisk (*).
Groupabcdef
b0.0003 *
c0.0444 *0.462
d0.0000 *0.0000 *0.0000 *
e0.0039 *0.19610.23380.0000 *
f0.32530.0129 *0.05170.0000 *0.0093 *
g0.0000 *0.0005 *0.0050 *0.0008 *0.03230.0000 *
Table 3. Results of Pearson correlation test for rainfall, atmospheric pressure, cloud cover, lunar phase, water discharge of Cassarate stream (Q Cassarate), and water discharge of Vedeggio stream (Q Vedeggio). p-values showing a statistical significative correlation (p < 0.05) are marked with an asterisk (*).
Table 3. Results of Pearson correlation test for rainfall, atmospheric pressure, cloud cover, lunar phase, water discharge of Cassarate stream (Q Cassarate), and water discharge of Vedeggio stream (Q Vedeggio). p-values showing a statistical significative correlation (p < 0.05) are marked with an asterisk (*).
RainfallPressureCloud CoverLunar PhaseQ (Cassarate)Q (Vedeggio)
Tagrp-Valuerp-Valuerp-Valuerp-Valuerp-Valuerp-Value
12510−0.0910.2490.0210.794−0.0240.758−0.274<0.01 *0.0670.3990.0420.598
125110.0190.8060.0610.441−0.0260.740.25<0.01 *0.0330.6760.0680.392
125150.070.3790.239<0.01 *−0.1050.183−0.1690.032 *−0.0140.8620.0110.889
12521−0.0670.395−0.0040.957−0.0520.5130.0720.365−0.1810.021 *−0.1210.124
12523−0.0130.8730.324<0.01 *−0.1270.1070.1250.112−0.080.315−0.070.379
125250.0190.815−0.0440.5790.030.702−0.040.6130.1780.024 *0.1060.18
12528−0.0880.2650.0080.9230.2020.010 *−0.1430.070.030.702−0.0310.694
125290.1640.037 *−0.110.1640.2060.0090.1310.0980.1160.1420.0690.385
125320.0470.5560.215<0.01 *0.0050.952−0.050.532−0.0610.4390.0730.355
125400.1260.11−0.0530.50.284<0.01 *−0.1070.1770.0670.395−0.0270.733
125430.0090.914−0.0220.7860.0630.4280.0560.4770.050.5320.0290.716
125500.0510.519−0.0790.3180.0070.930.0940.2320.0470.5490.0570.474
125510.0150.846−0.0260.7450.0510.519−0.0520.513−0.080.311−0.1190.131
12552−0.0690.383−0.0530.504−0.0140.855−0.0660.402−0.1840.019 *−0.206<0.01 *
12555−0.0160.8390.1520.0540.0420.596−0.0470.5490.0350.658−0.0320.687
12582−0.0130.867−0.271<0.01 *0.0720.3630.0290.71−0.050.529−0.0060.937
12593−0.0080.9190.0730.356−0.0140.8580.0620.432−0.0560.483−0.0920.245
126000.0850.28−0.1240.1170.0340.6670.1110.1580.0930.2410.1040.188
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Brignone, S.; Minazzi, L.; Molina, C.; Putelli, T.; Volta, P. How Much Hatchery-Reared Brown Trout Move in a Large, Deep Subalpine Lake? An Acoustic Telemetry Study. Environments 2024, 11, 245. https://doi.org/10.3390/environments11110245

AMA Style

Brignone S, Minazzi L, Molina C, Putelli T, Volta P. How Much Hatchery-Reared Brown Trout Move in a Large, Deep Subalpine Lake? An Acoustic Telemetry Study. Environments. 2024; 11(11):245. https://doi.org/10.3390/environments11110245

Chicago/Turabian Style

Brignone, Stefano, Luca Minazzi, Christophe Molina, Tiziano Putelli, and Pietro Volta. 2024. "How Much Hatchery-Reared Brown Trout Move in a Large, Deep Subalpine Lake? An Acoustic Telemetry Study" Environments 11, no. 11: 245. https://doi.org/10.3390/environments11110245

APA Style

Brignone, S., Minazzi, L., Molina, C., Putelli, T., & Volta, P. (2024). How Much Hatchery-Reared Brown Trout Move in a Large, Deep Subalpine Lake? An Acoustic Telemetry Study. Environments, 11(11), 245. https://doi.org/10.3390/environments11110245

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