# An individual-based model to predict the spatial distribution of northern pike, European perch and pikeperch from habitat use in a reservoir subject to water level fluctuations

Paul Miguet, Laurence Tissot, Patrick Lambert, Ine S Pauwels, Samuel Westrelin

► To cite this version:

Paul Miguet, Laurence Tissot, Patrick Lambert, Ine S Pauwels, Samuel Westrelin. An individual-based model to predict the spatial distribution of northern pike, European perch and pikeperch from habitat use in a reservoir subject to water level fluctuations. Hydroécologie Appliquée, 2024, 24, pp.10.1051/hydro/2024001. hal-04541396

HAL Id: hal-04541396

https://hal.inrae.fr/hal-04541396

Submitted on 10 Apr 2024

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# An individual-based model to predict the spatial distribution of northern pike, European perch and pikeperch from habitat use in a reservoir subject to water level fluctuations

Paul Miguet, Laurence Tissot, Patrick Lambert, Ine S Pauwels, Samuel Westrelin

► To cite this version:

Paul Miguet, Laurence Tissot, Patrick Lambert, Ine S Pauwels, Samuel Westrelin. An individual-based model to predict the spatial distribution of northern pike, European perch and pikeperch from habitat use in a reservoir subject to water level fluctuations. Hydroécologie Appliquée, 2024, 24, pp.10.1051/hydro/2024001. hal-04541396

HAL Id: hal-04541396

https://hal.inrae.fr/hal-04541396v1

Submitted on 10 Apr 2024

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

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

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

Distributed under a Creative Commons Attribution 4.0 International License
# An individual-based model to predict the spatial distribution of northern pike, European perch and pikeperch from habitat use in a reservoir subject to water level fluctuations

## *Un modèle individu-centré pour prédire la distribution spatiale du brochet, de la perche commune et du sandre, à partir de l'utilisation de l'habitat dans un réservoir soumis à des fluctuations de niveau d'eau*

Paul Miguet<fnref n="1" />, Laurence Tissot<fnref n="2" />, Patrick Lambert<fnref n="3" />,
Ine S. Pauwels<fnref n="4" />, Samuel Westrelin<fnref n="1" />,*

**Abstract** — Reservoirs are subject to artificial water level fluctuations leading to a degradation of the littoral habitats. This often results in reduced productivity and biological diversity, particularly for fish. The development of predictive tools of fish population dynamics linked to habitat characteristics can help to optimize management actions such as habitat restoration. We developed a spatially explicit individual-based model, the *3Plake* model, which predicts the spatial distribution of three fish species, northern pike, European perch and pikeperch, over seasons and diel cycles according to their habitat use. The model was calibrated with an accurate description of habitat and high spatial and temporal resolution data of fish movements in a French reservoir. The model calibration is only based on a species-specific coefficient of habitat selectivity. It was chosen to (i) match the observed and simulated traveled hourly and daily distances and (ii) minimize a metric quantifying the distance between observed and simulated density maps. The *3Plake* model correctly simulated the means of traveled distances and the species distributions along the year on the study reservoir. The minimal input required (HSI maps and maximum distance range) allows its easy implementation on other reservoirs and fish species. It could be used to assess the impact of habitat restoration or water level management scenarios on fish distribution.

**Keywords** — Spatially explicit modeling, fish movement, habitat selection, acoustic telemetry.

<footnote n="1">¹ INRAE, Aix Marseille Univ, Pôle R&D ECLA, HYNES (INRAE-EDF R&D), RECOVER, 3275 Route de Cézanne - CS 40061, 13182 Aix-en-Provence Cedex 5, France</footnote>

<footnote n="2">² EDF R&D LNHE - Laboratoire National d'Hydraulique et Environnement, HYNES (INRAE-EDF R&D), 6 quai Watier, 78400 Chatou, France</footnote>

<footnote n="3">³ INRAE, EABX, HYNES (INRAE-EDF R&D) 50 avenue de Verdun Gazinet, 33612 Cestas cedex, France</footnote>

<footnote n="4">⁴ Research Institute for Nature and Forest, Team Aquatic Management, Havenlaan 88/bus 73, 1000 Brussels, Belgium</footnote>

<footnote>*e-mail: samuel.westrelin@inrae.fr</footnote>
# An individual-based model to predict the spatial distribution of northern pike, European perch and pikeperch from habitat use in a reservoir subject to water level fluctuations

## *Un modèle individu-centré pour prédire la distribution spatiale du brochet, de la perche commune et du sandre, à partir de l'utilisation de l'habitat dans un réservoir soumis à des fluctuations de niveau d'eau*

Paul Miguet<fnref n="1" />, Laurence Tissot<fnref n="2" />, Patrick Lambert<fnref n="3" />, Ine S. Pauwels<fnref n="4" />, Samuel Westrelin<fnref n="1" />,*

**Abstract** — Reservoirs are subject to artificial water level fluctuations leading to a degradation of the littoral habitats. This often results in reduced productivity and biological diversity, particularly for fish. The development of predictive tools of fish population dynamics linked to habitat characteristics can help to optimize management actions such as habitat restoration. We developed a spatially explicit individual-based model, the *3Plake* model, which predicts the spatial distribution of three fish species, northern pike, European perch and pikeperch, over seasons and diel cycles according to their habitat use. The model was calibrated with an accurate description of habitat and high spatial and temporal resolution data of fish movements in a French reservoir. The model calibration is only based on a species-specific coefficient of habitat selectivity. It was chosen to (i) match the observed and simulated traveled hourly and daily distances and (ii) minimize a metric quantifying the distance between observed and simulated density maps. The *3Plake* model correctly simulated the means of traveled distances and the species distributions along the year on the study reservoir. The minimal input required (HSI maps and maximum distance range) allows its easy implementation on other reservoirs and fish species. It could be used to assess the impact of habitat restoration or water level management scenarios on fish distribution.

**Keywords** — Spatially explicit modeling, fish movement, habitat selection, acoustic telemetry.

<footnote n="1">¹ INRAE, Aix Marseille Univ, Pôle R&D ECLA, HYNES (INRAE-EDF R&D), RECOVER, 3275 Route de Cézanne - CS 40061, 13182 Aix-en-Provence Cedex 5, France</footnote>

<footnote n="2">² EDF R&D LNHE - Laboratoire National d'Hydraulique et Environnement, HYNES (INRAE-EDF R&D), 6 quai Watier, 78400 Chatou, France</footnote>

<footnote n="3">³ INRAE, EABX, HYNES (INRAE-EDF R&D) 50 avenue de Verdun Gazinet, 33612 Cestas cedex, France</footnote>

<footnote n="4">⁴ Research Institute for Nature and Forest, Team Aquatic Management, Havenlaan 88/bus 73, 1000 Brussels, Belgium</footnote>

<footnote>*e-mail: samuel.westrelin@inrae.fr</footnote>
**Résumé** – Les réservoirs sont soumis à des fluctuations artificielles du niveau de l'eau entraînant une dégradation des habitats littoraux. Elles entraînent souvent une réduction de la productivité et de la diversité biologiques, en particulier pour les poissons. Le développement d'outils prédictifs de la dynamique des populations de poissons, liés aux caractéristiques de l'habitat, peut aider à optimiser les actions de gestion telles que les mesures de restauration ou de gestion des niveaux d'eau. Nous avons développé un modèle individu-centré spatialement explicite, le modèle 3Plake, qui prédit la distribution spatiale de trois espèces de poissons, le brochet, la perche commune et le sandre, au fil des saisons et du cycle diurne en fonction de leur utilisation de l'habitat. Le modèle a été calibré grâce à une description précise de l'habitat et des données à haute résolution spatiale et temporelle des déplacements des poissons dans une retenue française. La calibration du modèle est uniquement basée sur un coefficient de sélectivité de l'habitat spécifique à l'espèce. Ce coefficient a été choisi pour (i) faire correspondre les distances parcourues horaires et journalières observées et simulées et (ii) minimiser une métrique quantifiant la distance entre les cartes de densité observées et simulées. Le modèle 3Plake a simulé correctement les moyennes des distances parcourues et les distributions des espèces tout au long de l'année sur la retenue d'étude. Le minimum de données d'entrée requises permet d'envisager des transpositions sur d'autres réservoirs et espèces piscicoles. Il pourrait être utilisé pour évaluer l'impact de mesures de restauration de l'habitat ou de scénarios de gestion des niveaux d'eau sur la répartition des poissons.

**Mots-clés** – Modélisation spatialement explicite, mouvement des poissons, selection de l'habitat, telemetrie acoustique.

# 1 Introduction

Reservoirs provide numerous services worldwide such as hydropower, water supply (e.g., irrigation, drinking water) or flood protection. With more than 45,000 large dams in the world at the beginning of the 20th century (Hjorth & Bengtsson, 2012), reservoirs need to be considered when managing global biodiversity (Liermann et al., 2012; McAllister et al., 2001; Wu et al., 2019). In reservoirs, and more generally in lentic ecosystems, littoral areas are characterized by a greater diversity of both abiotic and biotic structures than pelagic areas (Prchalová et al., 2009; Prchalová et al., 2008; Zohary & Gasith, 2014; Zohary & Ostrovsky, 2011). These structures constitute

complex habitats and even hot spots of biodiversity in lakes (Gasith & Gafny, 1998; Schmieder, 2004), providing a high availability of trophic resources (Pierce et al., 1994). Consequently, in lakes and reservoirs, the achievement of many species life cycle depends on the littoral area.

The anthropogenic uses of reservoirs lead to artificial water level fluctuations (WLF) which are very different from those of natural lakes (Wetzel, 1990). Artificial WLF affect littoral habitat structure by bank erosion (Carmignani & Roy, 2017; Hirsch et al., 2017) and by modification of substrate and macrophytes compositions (Evtimova & Donohue, 2014). They can also temporarily prevent fish from accessing breeding (Hudon et al., 2005) or
**Résumé** – Les réservoirs sont soumis à des fluctuations artificielles du niveau de l'eau entraînant une dégradation des habitats littoraux. Elles entraînent souvent une réduction de la productivité et de la diversité biologiques, en particulier pour les poissons. Le développement d'outils prédictifs de la dynamique des populations de poissons, liés aux caractéristiques de l'habitat, peut aider à optimiser les actions de gestion telles que les mesures de restauration ou de gestion des niveaux d'eau. Nous avons développé un modèle individu-centré spatialement explicite, le modèle 3Plake, qui prédit la distribution spatiale de trois espèces de poissons, le brochet, la perche commune et le sandre, au fil des saisons et du cycle diurne en fonction de leur utilisation de l'habitat. Le modèle a été calibré grâce à une description précise de l'habitat et des données à haute résolution spatiale et temporelle des déplacements des poissons dans une retenue française. La calibration du modèle est uniquement basée sur un coefficient de sélectivité de l'habitat spécifique à l'espèce. Ce coefficient a été choisi pour (i) faire correspondre les distances parcourues horaires et journalières observées et simulées et (ii) minimiser une métrique quantifiant la distance entre les cartes de densité observées et simulées. Le modèle 3Plake a simulé correctement les moyennes des distances parcourues et les distributions des espèces tout au long de l'année sur la retenue d'étude. Le minimum de données d'entrée requises permet d'envisager des transpositions sur d'autres réservoirs et espèces piscicoles. Il pourrait être utilisé pour évaluer l'impact de mesures de restauration de l'habitat ou de scénarios de gestion des niveaux d'eau sur la répartition des poissons.

**Mots-clés** – Modélisation spatialement explicite, mouvement des poissons, selection de l'habitat, telemetrie acoustique.

# 1 Introduction

Reservoirs provide numerous services worldwide such as hydropower, water supply (e.g., irrigation, drinking water) or flood protection. With more than 45,000 large dams in the world at the beginning of the 20th century (Hjorth & Bengtsson, 2012), reservoirs need to be considered when managing global biodiversity (Liermann et al., 2012; McAllister et al., 2001; Wu et al., 2019). In reservoirs, and more generally in lentic ecosystems, littoral areas are characterized by a greater diversity of both abiotic and biotic structures than pelagic areas (Prchalová et al., 2009; Prchalová et al., 2008; Zohary & Gasith, 2014; Zohary & Ostrovsky, 2011). These structures constitute

complex habitats and even hot spots of biodiversity in lakes (Gasith & Gafny, 1998; Schmieder, 2004), providing a high availability of trophic resources (Pierce et al., 1994). Consequently, in lakes and reservoirs, the achievement of many species life cycle depends on the littoral area.

The anthropogenic uses of reservoirs lead to artificial water level fluctuations (WLF) which are very different from those of natural lakes (Wetzel, 1990). Artificial WLF affect littoral habitat structure by bank erosion (Carmignani & Roy, 2017; Hirsch et al., 2017) and by modification of substrate and macrophytes compositions (Evtimova & Donohue, 2014). They can also temporarily prevent fish from accessing breeding (Hudon et al., 2005) or
wintering habitats (Cott *et al.*, 2008) and resting or refuge areas (Kaczka & Miranda, 2014; Logez *et al.*, 2016). Thus, littoral area concentrates a lot of restoration projects to sustain fish fauna by improving habitat, e.g., through revegetation of banks, creation of shallow zones with habitat enhancement or deployment of floating islands (de Moraes *et al.*, 2023; Halleraker *et al.*, 2016; Radinger *et al.*, 2023). Their success often remains uncertain (Vanderbosch & Galatowitsch, 2010), in part because it is dependent on multiple underlying management decisions, environmental conditions and difficulties to conduct such an evaluation. Predictive tools of fish population dynamics are valuable for improving success of restoration projects by testing the effects of different scenario, but they are still sparse (Strayer & Findlay, 2010).

Beyond specific reservoir ecosystems, many studies revealed the key role of habitat in population dynamics. Habitat quality drives the demographic parameters of fish populations, such as fecundity, growing or reproductive success (Farò *et al.*, 2021; Hayes *et al.*, 2009). To seek for an optimized physical habitat quality and availability, habitat is also involved in individual movements (Morales & Ellner, 2002; Nathan *et al.*, 2008; Patterson *et al.*, 2008). Finally, these movements contribute to the spatial distribution of species densities (Blanchard *et al.*, 2021). Then, the modelling of individual fish movements frequently uses habitat

quality as a main driver (Hovel & Regan, 2008; Railsback *et al.*, 1999; Van Winkle *et al.*, 1998).

In this study, we applied an individual-based approach to model fish movements in relation to habitat use. The individual approach is appropriate to simulate movements at the same resolution than habitat use (Grimm *et al.*, 2006; Railsback *et al.*, 1999). The main objective was to represent the spatial distribution of fish in a reservoir subject to water level fluctuations which frequently modify available habitats. The second objective was to keep a relatively simple calibration to facilitate future applications to other reservoirs concerned with restoration projects. The developed model, the 3PLake model, was supported by a high resolution dataset collected during previous studies on northern pike (Esox Lucius), European perch (Perca fluviatilis) and pikeperch (Sander lucio-perca), three predatory fish species coexisting in a French reservoir (Roy, 2014; Westrelin *et al.*, 2018). These species, common in European reservoirs (Overton *et al.*, 2015; Skov & Nilsson, 2018), show contrasted habitat preferences. Northern pike are mainly littoral (Chapman & Mackay, 1984; Craig, 2008), European perch are more generalist (Imbrock *et al.*, 2005; Pekcan-Hekim *et al.*, 2005) and pikeperch are rather pelagic (Vehanen & Lahti, 2003). We hypothesized that these species showed different spatial distribution in relation with their habitat use and selectivity.
wintering habitats (Cott *et al.*, 2008) and resting or refuge areas (Kaczka & Miranda, 2014; Logez *et al.*, 2016). Thus, littoral area concentrates a lot of restoration projects to sustain fish fauna by improving habitat, e.g., through revegetation of banks, creation of shallow zones with habitat enhancement or deployment of floating islands (de Moraes *et al.*, 2023; Halleraker *et al.*, 2016; Radinger *et al.*, 2023). Their success often remains uncertain (Vanderbosch & Galatowitsch, 2010), in part because it is dependent on multiple underlying management decisions, environmental conditions and difficulties to conduct such an evaluation. Predictive tools of fish population dynamics are valuable for improving success of restoration projects by testing the effects of different scenario, but they are still sparse (Strayer & Findlay, 2010).

Beyond specific reservoir ecosystems, many studies revealed the key role of habitat in population dynamics. Habitat quality drives the demographic parameters of fish populations, such as fecundity, growing or reproductive success (Farò *et al.*, 2021; Hayes *et al.*, 2009). To seek for an optimized physical habitat quality and availability, habitat is also involved in individual movements (Morales & Ellner, 2002; Nathan *et al.*, 2008; Patterson *et al.*, 2008). Finally, these movements contribute to the spatial distribution of species densities (Blanchard *et al.*, 2021). Then, the modelling of individual fish movements frequently uses habitat

quality as a main driver (Hovel & Regan, 2008; Railsback *et al.*, 1999; Van Winkle *et al.*, 1998).

In this study, we applied an individual-based approach to model fish movements in relation to habitat use. The individual approach is appropriate to simulate movements at the same resolution than habitat use (Grimm *et al.*, 2006; Railsback *et al.*, 1999). The main objective was to represent the spatial distribution of fish in a reservoir subject to water level fluctuations which frequently modify available habitats. The second objective was to keep a relatively simple calibration to facilitate future applications to other reservoirs concerned with restoration projects. The developed model, the 3PLake model, was supported by a high resolution dataset collected during previous studies on northern pike (Esox Lucius), European perch (Perca fluviatilis) and pikeperch (Sander lucio-perca), three predatory fish species coexisting in a French reservoir (Roy, 2014; Westrelin *et al.*, 2018). These species, common in European reservoirs (Overton *et al.*, 2015; Skov & Nilsson, 2018), show contrasted habitat preferences. Northern pike are mainly littoral (Chapman & Mackay, 1984; Craig, 2008), European perch are more generalist (Imbrock *et al.*, 2005; Pekcan-Hekim *et al.*, 2005) and pikeperch are rather pelagic (Vehanen & Lahti, 2003). We hypothesized that these species showed different spatial distribution in relation with their habitat use and selectivity.
# 2 Material and methods

## 2.1 Study site

The Bariousses hydropower reservoir (45.33°N, 1.49°E) is located on the Vézère River in the western central part of France. The water level (WL) varied between altitudes 507.0 m and 513.5 m during the study from March 2012 to March 2014 (Fig. A.1 Appendix A). At the mean WL (511.5 m), the reservoir area was 0.866 km², mean depth 7.1 m and maximum depth 19.4 m.

Based on the Secchi transparency depth, the littoral area of the reservoir was defined as the area connected to the bank with a depth lower than 2.5 m. The littoral area varied from 19.5% at low WL (507.0 m) to 13.3% at high WL (513.5 m) of the total reservoir area. Its habitats tended to become more homogeneous with a lowering structural complexity when the WL dropped: while gravel/ pebble, immersed grass and emerging trees dominated at high levels, silt covered more than half the littoral area at low levels and emerging trees shrank (see Roy *et al.*, 2021; and Westrelin *et al.*, 2018 for a detailed description).

The ichtyofauna was dominated by two cyprinids, roach (*Rutilus rutilus*) and common bream (*Abramis brama*), and three percids, ruffe (*Gymnocephalus cernua*), European perch and pike-perch. Northern pike was another common piscivorous species living in the reservoir (Westrelin *et al.*, 2021).

## 2.2 Species of interest

Northern pike (hereafter named pike) are an ambush predator preferring highly structured areas with vegetation (Chapman & Mackay, 1984; Cook & Bergersen,

1988; Keith *et al.*, 2011): in lakes, they prefer the littoral zone and shallow waters (Craig, 1996; Westrelin *et al.*, 2021), with some versatility observed among sites and sometimes a territorial behaviour (Eklöv, 1992; Skov & Koed, 2004). Pike are mostly active during day or twilight (Baktoft *et al.*, 2012; Beaumont *et al.*, 2005; Cook & Bergersen, 1988; Kobler *et al.*, 2008). Some authors mention seasonal differences with a daytime activity in winter and a twilight activity in summer (Baktoft *et al.*, 2012; Kobler *et al.*, 2008; Ríha *et al.*, 2022). In addition, the structural complexity of habitats influences foraging behaviour and activity of pike (Ríha *et al.*, 2022).

European perch (hereafter named perch) frequent both littoral zones and neighboring deeper areas in summer depending on the phase of the day (Imbrock *et al.*, 2005; Pekcan-Hekim *et al.*, 2005; Westrelin *et al.*, 2021). They prefer deeper waters in winter (Thorpe, 1977). Young European perch have a group foraging behaviour but large European perch are rather solitary (Craig, 1987; Eklöv, 1992).

Pikeperch prefer deep pelagic areas (Vehanen & Lahti 2003; Westrelin *et al.*, 2021), to seek darkness (Craig, 1987) or prey (Huuskonen *et al.*, 2019). They migrate to deep waters in winter (Ríha *et al.*, 2022; Vehanen & Lahti, 2003). Pikeperch are mostly active at dusk (Craig, 2000; Horký *et al.*, 2008).

This knowledge highlights specific feeding strategies, habitat preferences or behaviour of the study species. Within each species, habitat preferences depend on the season (Ríha *et al.*, 2022; Westrelin *et al.*, 2021) and the activity shows a diel rhythm (Baktoft *et al.*, 2012; Horký *et al.*, 2008; Jacobsen *et al.*, 2015).
# 2 Material and methods

## 2.1 Study site

The Bariousses hydropower reservoir (45.33°N, 1.49°E) is located on the Vézère River in the western central part of France. The water level (WL) varied between altitudes 507.0 m and 513.5 m during the study from March 2012 to March 2014 (Fig. A.1 Appendix A). At the mean WL (511.5 m), the reservoir area was 0.866 km², mean depth 7.1 m and maximum depth 19.4 m.

Based on the Secchi transparency depth, the littoral area of the reservoir was defined as the area connected to the bank with a depth lower than 2.5 m. The littoral area varied from 19.5% at low WL (507.0 m) to 13.3% at high WL (513.5 m) of the total reservoir area. Its habitats tended to become more homogeneous with a lowering structural complexity when the WL dropped: while gravel/ pebble, immersed grass and emerging trees dominated at high levels, silt covered more than half the littoral area at low levels and emerging trees shrank (see Roy *et al.*, 2021; and Westrelin *et al.*, 2018 for a detailed description).

The ichtyofauna was dominated by two cyprinids, roach (*Rutilus rutilus*) and common bream (*Abramis brama*), and three percids, ruffe (*Gymnocephalus cernua*), European perch and pike-perch. Northern pike was another common piscivorous species living in the reservoir (Westrelin *et al.*, 2021).

## 2.2 Species of interest

Northern pike (hereafter named pike) are an ambush predator preferring highly structured areas with vegetation (Chapman & Mackay, 1984; Cook & Bergersen,

1988; Keith *et al.*, 2011): in lakes, they prefer the littoral zone and shallow waters (Craig, 1996; Westrelin *et al.*, 2021), with some versatility observed among sites and sometimes a territorial behaviour (Eklöv, 1992; Skov & Koed, 2004). Pike are mostly active during day or twilight (Baktoft *et al.*, 2012; Beaumont *et al.*, 2005; Cook & Bergersen, 1988; Kobler *et al.*, 2008). Some authors mention seasonal differences with a daytime activity in winter and a twilight activity in summer (Baktoft *et al.*, 2012; Kobler *et al.*, 2008; Ríha *et al.*, 2022). In addition, the structural complexity of habitats influences foraging behaviour and activity of pike (Ríha *et al.*, 2022).

European perch (hereafter named perch) frequent both littoral zones and neighboring deeper areas in summer depending on the phase of the day (Imbrock *et al.*, 2005; Pekcan-Hekim *et al.*, 2005; Westrelin *et al.*, 2021). They prefer deeper waters in winter (Thorpe, 1977). Young European perch have a group foraging behaviour but large European perch are rather solitary (Craig, 1987; Eklöv, 1992).

Pikeperch prefer deep pelagic areas (Vehanen & Lahti 2003; Westrelin *et al.*, 2021), to seek darkness (Craig, 1987) or prey (Huuskonen *et al.*, 2019). They migrate to deep waters in winter (Ríha *et al.*, 2022; Vehanen & Lahti, 2003). Pikeperch are mostly active at dusk (Craig, 2000; Horký *et al.*, 2008).

This knowledge highlights specific feeding strategies, habitat preferences or behaviour of the study species. Within each species, habitat preferences depend on the season (Ríha *et al.*, 2022; Westrelin *et al.*, 2021) and the activity shows a diel rhythm (Baktoft *et al.*, 2012; Horký *et al.*, 2008; Jacobsen *et al.*, 2015).
## 2.3 Tracking data

From March 2012 to March 2014, the three species were tracked by an array of 40 underwater VR2W 69kHz acoustic receivers in the Bariousses reservoir (France). Vemco V9P-2L (47 mm long, 6.3 g in the air) and V8-4L (20.5 mm long, 2 g in the air) acoustic transmitters were used. Specifically, data from 10 Northern pike, 22 European perch and 26 pikeperch (Appendix B) were retrieved to define habitat use and preferences based on their observed positions (Westrelin et al., 2021) and information on the littoral habitat as described in Westrelin et al. (2018). Detailed information on the tracking system and the tagging procedure is provided in Roy (2014), Roy et al., (2014) and Westrelin et al. (2018).

These tracking data were used to define, for each species, hourly and daily traveled distances, maps of habitat suitability index (HSI) that quantified the attractiveness of each reservoir location according to season and water level, and a density map.

Mean habitat preferences for the three species were quantified by selection ratios of various habitat characteristics: water depth over the whole lake, main substrate type, overhanging vegetation, tree stumps and emerging trees in the littoral zone. They were computed for each season, the season having a strong effect, and for each water level from altitude 507.0 to 513.5 m by 0.1 m. For details on this method, see Westrelin et al. (2018). For each species, these selection ratios were aggregated for each combination of season and water level in a HSI

index that could be mapped over the lake on a 10 m x 10 m grid (264 maps per species, see Appendix C).

## 2.4 Model

The 3PLake model has been developed on the basis of a telemetry dataset with the willingness to use the fewest possible parameterization in order to avoid too much tuning and to facilitate the reproducibility. Only one parameter, the *p* selectivity coefficient, needs to be calibrated. The description of the 3PLake model below draws heavily from the ODD protocol (Overview, Design concepts, Details), but in a short version fitting to the relative simplicity of our model (Grimm et al., 2010; Grimm et al., 2020).

The 3PLake model has been developed in Java using SimAquaLife framework (Dumoulin, 2007).

### 2.4.1 Purpose

The purpose of the 3PLake model is to simulate individual movements of three fish species (pike, perch, pikeperch) depending on the season and the phase of the day that influence the activity of species depending on their diel cycle and the available habitat (synthesized in an HSI index based on water depth and littoral characteristics of substrate type, overhanging vegetation, tree stumps and emerging trees) (Fig. 1). Its objective is to predict the spatial distribution of fish individuals depending on the habitat availability which is closely linked to the water level. In this case study, the model was calibrated in the Bariousses reservoir.
## 2.3 Tracking data

From March 2012 to March 2014, the three species were tracked by an array of 40 underwater VR2W 69kHz acoustic receivers in the Bariousses reservoir (France). Vemco V9P-2L (47 mm long, 6.3 g in the air) and V8-4L (20.5 mm long, 2 g in the air) acoustic transmitters were used. Specifically, data from 10 Northern pike, 22 European perch and 26 pikeperch (Appendix B) were retrieved to define habitat use and preferences based on their observed positions (Westrelin et al., 2021) and information on the littoral habitat as described in Westrelin et al. (2018). Detailed information on the tracking system and the tagging procedure is provided in Roy (2014), Roy et al., (2014) and Westrelin et al. (2018).

These tracking data were used to define, for each species, hourly and daily traveled distances, maps of habitat suitability index (HSI) that quantified the attractiveness of each reservoir location according to season and water level, and a density map.

Mean habitat preferences for the three species were quantified by selection ratios of various habitat characteristics: water depth over the whole lake, main substrate type, overhanging vegetation, tree stumps and emerging trees in the littoral zone. They were computed for each season, the season having a strong effect, and for each water level from altitude 507.0 to 513.5 m by 0.1 m. For details on this method, see Westrelin et al. (2018). For each species, these selection ratios were aggregated for each combination of season and water level in a HSI

index that could be mapped over the lake on a 10 m x 10 m grid (264 maps per species, see Appendix C).

## 2.4 Model

The 3PLake model has been developed on the basis of a telemetry dataset with the willingness to use the fewest possible parameterization in order to avoid too much tuning and to facilitate the reproducibility. Only one parameter, the *p* selectivity coefficient, needs to be calibrated. The description of the 3PLake model below draws heavily from the ODD protocol (Overview, Design concepts, Details), but in a short version fitting to the relative simplicity of our model (Grimm et al., 2010; Grimm et al., 2020).

The 3PLake model has been developed in Java using SimAquaLife framework (Dumoulin, 2007).

### 2.4.1 Purpose

The purpose of the 3PLake model is to simulate individual movements of three fish species (pike, perch, pikeperch) depending on the season and the phase of the day that influence the activity of species depending on their diel cycle and the available habitat (synthesized in an HSI index based on water depth and littoral characteristics of substrate type, overhanging vegetation, tree stumps and emerging trees) (Fig. 1). Its objective is to predict the spatial distribution of fish individuals depending on the habitat availability which is closely linked to the water level. In this case study, the model was calibrated in the Bariousses reservoir.
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Flow diagram of processes included in the 3Plate model. Input data are represented in green ellipses (dotted arrows indicate where they occurred). $T_{max}$ is the number of hourly time steps; $D_{max}$ is the hourly distance range; $N_{ind}$ is the number of individuals in the simulation; N Rep is the number of replicates of the simulation.</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Flow diagram of processes included in the 3Plate model. Input data are represented in green ellipses (dotted arrows indicate where they occurred). $T_{max}$ is the number of hourly time steps; $D_{max}$ is the hourly distance range; $N_{ind}$ is the number of individuals in the simulation; N Rep is the number of replicates of the simulation.</figcaption></figure>
### 2.4.2 Entities, state variables, and scales

The reservoir area was discretized into 10 m x 10 m grid cells.

The entities are the three fish species of interest. Their movements depend on their habitat use and the habitat availability.

The time step is 1 h. The shift between seasons depends on the date: spring (from March 20 to 20 June), summer (from 21 June to 21 September), autumn (from 22 September to 20 December) and winter (from 21 December to 19 March). The phase of the day (dawn, day, dusk, night) changes with the hour according to sunrise/sunset hours specific to the date. Dawn, that includes the hour preceding the sunrise hour, the sunrise hour itself and the following hour, and dusk, that includes the hour preceding the sunset hour, the sunset hour itself and the following hour, both lasted 3 h.

The model is designed to be applied on an annual cycle. The number of individuals is kept constant in all simulations with no birth and no death.

### 2.4.3 Process overview and scheduling

One typical realistic year has been built for which at each hour, the season was set from the breakdown exposed in 2.4.2, the phase of the day from sunrise/sunset hours in 2012 and the water level from the time series observed in 2013. For each season combined to each water level and to each species corresponded a HSI map. For each season combined to each phase of the day and each species corresponded an hourly distance range that depends on the level of activity of

the species and that defines available cells in which to move in the next hour. HSI maps and hourly distance range, associated with a selectivity coefficient ($p$) (more details in paragraph 2.4.6), drive individual fish movement.

The three species were modeled in independent simulations integrating all individuals from one species.

### 2.4.4 Design concepts

At each time step, individuals move to a cell which is in the hourly distance range from the previous occupied cell. It depends on the attractiveness of the cell for the chosen species, quantified by the HSI map, and on the $p$ selectivity coefficient. The fish movement is assumed to be stochastic among the best available HSI cells. At each time step, the cell in which to move is randomly chosen among a fraction $p$ (the selectivity coefficient) of the best HSI cells individual could access, depending on the hourly distance range. Individuals can stay in their initial cell, without moving: it has the same status as other cells, it is only described by its HSI value.

There is no interaction between individuals for perch and pikeperch. To consider the territorial behaviour of pike, two individuals of this species cannot share a same cell.

The position (a cell identifier) of each individual fish is recorded at each time step (a date and an hour).

### 2.4.5 Initialization

A total of 50 individuals, with similar characteristics to those given in Appendix B, is included in the simulation. This is based on an estimate made from a full draining of the reservoir in
## 2.4.2 Entities, state variables, and scales

The reservoir area was discretized into 10 m x 10 m grid cells.

The entities are the three fish species of interest. Their movements depend on their habitat use and the habitat availability.

The time step is 1 h. The shift between seasons depends on the date: spring (from March 20 to 20 June), summer (from 21 June to 21 September), autumn (from 22 September to 20 December) and winter (from 21 December to 19 March). The phase of the day (dawn, day, dusk, night) changes with the hour according to sunrise/sunset hours specific to the date. Dawn, that includes the hour preceding the sunrise hour, the sunrise hour itself and the following hour, and dusk, that includes the hour preceding the sunset hour, the sunset hour itself and the following hour, both lasted 3 h.

The model is designed to be applied on an annual cycle. The number of individuals is kept constant in all simulations with no birth and no death.

## 2.4.3 Process overview and scheduling

One typical realistic year has been built for which at each hour, the season was set from the breakdown exposed in 2.4.2, the phase of the day from sunrise/sunset hours in 2012 and the water level from the time series observed in 2013. For each season combined to each water level and to each species corresponded a HSI map. For each season combined to each phase of the day and each species corresponded an hourly distance range that depends on the level of activity of

the species and that defines available cells in which to move in the next hour. HSI maps and hourly distance range, associated with a selectivity coefficient ($p$) (more details in paragraph 2.4.6), drive individual fish movement.

The three species were modeled in independent simulations integrating all individuals from one species.

## 2.4.4 Design concepts

At each time step, individuals move to a cell which is in the hourly distance range from the previous occupied cell. It depends on the attractiveness of the cell for the chosen species, quantified by the HSI map, and on the $p$ selectivity coefficient. The fish movement is assumed to be stochastic among the best available HSI cells. At each time step, the cell in which to move is randomly chosen among a fraction $p$ (the selectivity coefficient) of the best HSI cells individual could access, depending on the hourly distance range. Individuals can stay in their initial cell, without moving: it has the same status as other cells, it is only described by its HSI value.

There is no interaction between individuals for perch and pikeperch. To consider the territorial behaviour of pike, two individuals of this species cannot share a same cell.

The position (a cell identifier) of each individual fish is recorded at each time step (a date and an hour).

## 2.4.5 Initialization

A total of 50 individuals, with similar characteristics to those given in Appendix B, is included in the simulation. This is based on an estimate made from a full draining of the reservoir in
<table><caption>**Table I.** Input parameters of the 3Plake model and values used in the simulations for the case study.</caption><thead><tr><th>Name</th><th>Description</th><th>Value(s) in the simulations</th></tr></thead><tbody><tr><td>N<sub>ind</sub></td><td>Number of individuals in the simulation</td><td>50</td></tr><tr><td>T<sub>max</sub></td><td>Number of hourly time steps<br>(equivalent to the simulation<br>duration in hours)</td><td>8760 (24 h x 365 days),<br>i.e., 1 yr for each simulation</td></tr><tr><td>Sp</td><td>Species (one species per simulation)</td><td>pike, perch and pikeperch</td></tr><tr><td>p</td><td>Selectivity coefficient: fraction p of the<br>cells with the highest<br>HSI within the hourly distance range.<br>The selectivity is high for low values of p.</td><td>1% and then from 5% to 100%<br>by 5% increment (21 values)</td></tr><tr><td>N Rep</td><td>Number of replicates of the simulation</td><td>10</td></tr></tbody></table>

1997. Initialization is a stochastic process. The starting cell is chosen randomly among all cells.

### 2.4.6 Input parameters and input data

Five input parameters are used in the 3Plake model (Tab. I).

The *p* coefficient is the only parameter used to calibrate the model. The calibration procedure is detailed in the Model evaluation paragraph (2.4.8.).

In addition, the 3Plake model needs four input data:

* The *ephemeris* of the year to access to the sunrise and the sunset hours to define the phases of the day;

* The *hourly water level time series* (see Appendix A for this case study);

* The *HSI map* for each species and for each combination season\*water level (264 maps per species) (see Appendix C for this case study);

* The *hourly distance range* (*D*<sub>max</sub>) that defines the maximum distance between two consecutive positions of an individual in the model. It was calculated using the telemetry dataset. For each species, there is one value for each combination season\*-phase of the day (16 values per

species) (Appendix D for this case study). This hourly distance range is set as the mean hourly distance estimated over data multiplied by 1.7. This value was chosen (i) to simulate mean hourly distances that approached the empirical values and (ii) to get 80% of the observed hourly distances smaller than *D*<sub>max</sub> for all combinations of season\*-phase of the day.

### 2.4.7 Submodels

Environment and species characteristics are updated at each time step:

* The date and the hour define the current season and phase of the day;

* The water level changes following the hourly water level time series;

* The species HSI map changes depending on the season and the water level;

* The species *D*<sub>max</sub> is updated according to the season and phase of the day.

At each time step, individuals move to a cell within *D*<sub>max</sub> range from the previous position and which is in the lake at the water level corresponding to
<table><caption>**Table I.** Input parameters of the 3Plake model and values used in the simulations for the case study.</caption><thead><tr><th>Name</th><th>Description</th><th>Value(s) in the simulations</th></tr></thead><tbody><tr><td>N<sub>ind</sub></td><td>Number of individuals in the simulation</td><td>50</td></tr><tr><td>T<sub>max</sub></td><td>Number of hourly time steps<br>(equivalent to the simulation<br>duration in hours)</td><td>8760 (24 h x 365 days),<br>i.e., 1 yr for each simulation</td></tr><tr><td>Sp</td><td>Species (one species per simulation)</td><td>pike, perch and pikeperch</td></tr><tr><td>p</td><td>Selectivity coefficient: fraction p of the<br>cells with the highest<br>HSI within the hourly distance range.<br>The selectivity is high for low values of p.</td><td>1% and then from 5% to 100%<br>by 5% increment (21 values)</td></tr><tr><td>N Rep</td><td>Number of replicates of the simulation</td><td>10</td></tr></tbody></table>

1997. Initialization is a stochastic process. The starting cell is chosen randomly among all cells.

### 2.4.6 Input parameters and input data

Five input parameters are used in the 3Plake model (Tab. I).

The *p* coefficient is the only parameter used to calibrate the model. The calibration procedure is detailed in the Model evaluation paragraph (2.4.8.).

In addition, the 3Plake model needs four input data:

* The *ephemeris* of the year to access to the sunrise and the sunset hours to define the phases of the day;

* The *hourly water level time series* (see Appendix A for this case study);

* The *HSI map* for each species and for each combination season\*water level (264 maps per species) (see Appendix C for this case study);

* The *hourly distance range* (*D*<sub>max</sub>) that defines the maximum distance between two consecutive positions of an individual in the model. It was calculated using the telemetry dataset. For each species, there is one value for each combination season\*-phase of the day (16 values per

species) (Appendix D for this case study). This hourly distance range is set as the mean hourly distance estimated over data multiplied by 1.7. This value was chosen (i) to simulate mean hourly distances that approached the empirical values and (ii) to get 80% of the observed hourly distances smaller than *D*<sub>max</sub> for all combinations of season\*-phase of the day.

### 2.4.7 Submodels

Environment and species characteristics are updated at each time step:

* The date and the hour define the current season and phase of the day;

* The water level changes following the hourly water level time series;

* The species HSI map changes depending on the season and the water level;

* The species *D*<sub>max</sub> is updated according to the season and phase of the day.

At each time step, individuals move to a cell within *D*<sub>max</sub> range from the previous position and which is in the lake at the water level corresponding to
the time step. The candidate cells
depend on their attractiveness, quanti-
fied by their HSI value, and on the *p*
selectivity coefficient of the simulation.
A lower value of *p* corresponds to a
more selective habitat choice (towards
higher quality). A higher value of *p*
corresponds to a less selective habitat
choice; a value of 100% for *p* means
that the habitat choice is random
among the accessible cells.

We ran 10 replicates for each value of *p*, giving a total of 210 simulations per species (Tab. I). All simulations were run with 50 individuals and the time period covered lasted one year.

We calculated the following metrics
with simulation outputs:

* The hourly traveled distance, which is the Euclidean distance between two consecutively occupied cells.

* The daily traveled distance, which is the Euclidean distance between the cell of an individual at the first hour of a calendar day and the cell of the same individual 24 h later. The daily distance measures the net effect of the prospecting behaviour at a daily scale.

* The species density map, which is the total number of positions (from all individuals of the considered species) recorded in each cell over all time steps of a simulation.

2.4.8 Model evaluation

The traveled distance metrics were
used to evaluate the range of suitable *p*
values for each species. The objective
of the *3Plake* model was to simulate the
spatial distribution of each species.
Therefore, the comparison between
simulated and observed density maps
was used for the final calibration of *p* for
each species.

Hourly distance

Yearly mean hourly distance

For hourly distances, the evaluation
of the simulations was first based on a
comparison of the simulated means by
species (mean and range across the 10
replicates for each value of *p*) with the
observed means.

Because of non-continuous observed
data, the observed hourly distance was
calculated as the Euclidean distance
between the first position of an hour and
the first position of the next hour when
the time lag separating these positions
was between half an hour and one hour
and a half. We assigned a zero value to
all distances smaller or equal to 3 m,
which was the telemetry system mean
position error (Roy et al., 2014).

For a fair comparison of the simula-
tions with the observations, the yearly
mean observed hourly distance was
defined as the mean of the four seasonal
means to equally weigh the season.
Then we calculated a 95% confidence
interval for this observed mean using
bootstrap (1,000 resamplings with re-
placement; the 95% confidence interval
is thus given by the 25th and 975th values
sorted in increasing order).

For each species we identified an
optimal value of *p* simulating the least
biased mean hourly distance, i.e., the
value for which the mean of simulated
hourly distances was the closest to the
observed one (Westrelin et al., 2021).
We also calculated 1,000 bootstrap
values of the difference between the
observed and simulated yearly mean
hourly distances (resampling the indi-
viduals for the observed means, and
sampling randomly one of the 10
the time step. The candidate cells
depend on their attractiveness, quanti-
fied by their HSI value, and on the *p*
selectivity coefficient of the simulation.
A lower value of *p* corresponds to a
more selective habitat choice (towards
higher quality). A higher value of *p*
corresponds to a less selective habitat
choice; a value of 100% for *p* means
that the habitat choice is random
among the accessible cells.

We ran 10 replicates for each value of *p*, giving a total of 210 simulations per species (Tab. I). All simulations were run with 50 individuals and the time period covered lasted one year.

We calculated the following metrics
with simulation outputs:

* The hourly traveled distance, which is the Euclidean distance between two consecutively occupied cells.

* The daily traveled distance, which is the Euclidean distance between the cell of an individual at the first hour of a calendar day and the cell of the same individual 24 h later. The daily distance measures the net effect of the prospecting behaviour at a daily scale.

* The species density map, which is the total number of positions (from all individuals of the considered species) recorded in each cell over all time steps of a simulation.

2.4.8 Model evaluation

The traveled distance metrics were
used to evaluate the range of suitable *p*
values for each species. The objective
of the *3Plake* model was to simulate the
spatial distribution of each species.
Therefore, the comparison between
simulated and observed density maps
was used for the final calibration of *p* for
each species.

Hourly distance

Yearly mean hourly distance

For hourly distances, the evaluation
of the simulations was first based on a
comparison of the simulated means by
species (mean and range across the 10
replicates for each value of *p*) with the
observed means.

Because of non-continuous observed
data, the observed hourly distance was
calculated as the Euclidean distance
between the first position of an hour and
the first position of the next hour when
the time lag separating these positions
was between half an hour and one hour
and a half. We assigned a zero value to
all distances smaller or equal to 3 m,
which was the telemetry system mean
position error (Roy et al., 2014).

For a fair comparison of the simula-
tions with the observations, the yearly
mean observed hourly distance was
defined as the mean of the four seasonal
means to equally weigh the season.
Then we calculated a 95% confidence
interval for this observed mean using
bootstrap (1,000 resamplings with re-
placement; the 95% confidence interval
is thus given by the 25th and 975th values
sorted in increasing order).

For each species we identified an
optimal value of *p* simulating the least
biased mean hourly distance, i.e., the
value for which the mean of simulated
hourly distances was the closest to the
observed one (Westrelin et al., 2021).
We also calculated 1,000 bootstrap
values of the difference between the
observed and simulated yearly mean
hourly distances (resampling the indi-
viduals for the observed means, and
sampling randomly one of the 10
replicates for the simulated means). The percentiles 2.5 and 97.5 of these 1,000 values defined a 95% confidence interval of this difference. The difference between the observed and simulated yearly mean hourly distances was significant when this 95% confidence interval did not include zero.

## Hourly distance distribution

Then, for each species, we used boxplots to compare the distribution of simulated hourly distances for each value of *p* with that of observed ones.

For each species and each value of *p*, we built a boxplot with 40,000 simulated hourly distances that were randomly chosen out of all simulated replicates and time steps.

Additionally, we built a boxplot of observed hourly distances for each species. To equally weigh each season similarly to the simulations, we sampled 10,000 values with replacement for each of the four seasons, and we used these 40,000 values to build the boxplot.

## Daily distance

We calculated the observed daily distance with a duration separating both positions comprised between half a day and a day and a half, because of non-continuous observed data. We applied the same principles as for the hourly distances to compare simulated and observed daily distances.

## Density map

For the observed density map, we first calculated a density map for each season, for which the density in a cell

was the total number of positions recorded in this cell (corrected by the probability of positioning in this cell by the telemetry system, see Roy *et al.* (2014) for this probability map). Then, we calculated the yearly map as the cell-by-cell weighed mean of the four seasonal maps. The weights were the inverse of the total number of recorded positions in the season to give each season the same weight. See Appendix E.

Next, we centered and standardized the simulated and observed density maps considering only cells with a non-null probability of positioning by the telemetry system (Roy *et al.*, 2014). Then, for each cell, we computed the square differences of simulated and observed centered and standardized densities and summed them over all cells of the map. The Euclidean distance was the square root of this sum. Thus, for each species and each *p*, we obtained a mean and a range of this distance based on the 10 replicates. We also calculated a randomized model of this distance by randomizing the cell positions in the simulated density map before calculating its distance to the observed one (the used replicate of the simulation was randomly chosen each time). We repeated this randomization 1,000 times to calculate a mean and a 95% confidence interval. This bootstrap on the null model allowed to identify when the simulated distribution was closer to the observed distribution than would be a randomized distribution, in other words when the *3Plake* model was more informative than the null model.

For each species, we identified the value of *p* and its 95% confidence interval for this density map distance
replicates for the simulated means). The percentiles 2.5 and 97.5 of these 1,000 values defined a 95% confidence interval of this difference. The difference between the observed and simulated yearly mean hourly distances was significant when this 95% confidence interval did not include zero.

## Hourly distance distribution

Then, for each species, we used boxplots to compare the distribution of simulated hourly distances for each value of *p* with that of observed ones.

For each species and each value of *p*, we built a boxplot with 40,000 simulated hourly distances that were randomly chosen out of all simulated replicates and time steps.

Additionally, we built a boxplot of observed hourly distances for each species. To equally weigh each season similarly to the simulations, we sampled 10,000 values with replacement for each of the four seasons, and we used these 40,000 values to build the boxplot.

## Daily distance

We calculated the observed daily distance with a duration separating both positions comprised between half a day and a day and a half, because of non-continuous observed data. We applied the same principles as for the hourly distances to compare simulated and observed daily distances.

## Density map

For the observed density map, we first calculated a density map for each season, for which the density in a cell

was the total number of positions recorded in this cell (corrected by the probability of positioning in this cell by the telemetry system, see Roy *et al.* (2014) for this probability map). Then, we calculated the yearly map as the cell-by-cell weighed mean of the four seasonal maps. The weights were the inverse of the total number of recorded positions in the season to give each season the same weight. See Appendix E.

Next, we centered and standardized the simulated and observed density maps considering only cells with a non-null probability of positioning by the telemetry system (Roy *et al.*, 2014). Then, for each cell, we computed the square differences of simulated and observed centered and standardized densities and summed them over all cells of the map. The Euclidean distance was the square root of this sum. Thus, for each species and each *p*, we obtained a mean and a range of this distance based on the 10 replicates. We also calculated a randomized model of this distance by randomizing the cell positions in the simulated density map before calculating its distance to the observed one (the used replicate of the simulation was randomly chosen each time). We repeated this randomization 1,000 times to calculate a mean and a 95% confidence interval. This bootstrap on the null model allowed to identify when the simulated distribution was closer to the observed distribution than would be a randomized distribution, in other words when the *3Plake* model was more informative than the null model.

For each species, we identified the value of *p* and its 95% confidence interval for this density map distance
using bootstrap. For each value of *p*, we randomly sampled one value of the distance between density maps among the 10 replicates. Then we identified the value of *p* which led to the minimum distance for this sample. We repeated this 1,000 times to get 1,000 values for *p*. For each species, the best value of *p* was the mean across these 1,000 bootstrap values, and the 95% confidence interval was defined by the percentiles 2.5 and 97.5.

Finally, in addition to the density map
distance, we mapped the differences
between simulated and observed cen-
tered and standardized densities for
the optimal *p* value of each species to
spatially identify them.

3 Results

3.1 Hourly distance

Yearly mean of simulated hourly
distances increased with the selectivity
parameter *p*. The three species pre-
sented the same general pattern: a
steep rise for small values of *p* (up to 5-
10%) and then a slight linear increase
(Fig. 2 and Appendix F Fig. F1). The
simulated distances were very close
between the ten replicates (amplitude
less than 1 m). But there was a larger
uncertainty in the yearly mean of
observed hourly distances (i.e., a large
confidence interval; Fig. 2). Hence, the
mean of simulated distances was not
significantly different from the mean of
observed distances for a large range of
values of *p*. The numerical values
obtained by bootstrap for the *p* values
giving unbiased estimate of this yearly
mean were *p* = 70% (range 10–100%)
for pike, *p* = 60% (range 20–100%) for

perch and *p* = 30% (range 10–100%)
for pikeperch (Tab. II).

The distribution of simulated hourly
distances was less dispersed than that
of the observed ones whatever the value
of p in this range for all species (Fig. 2
and Appendix F Fig. F1). Specifically,
simulations captured fewer short dis-
tances (null distances in the observa-
tions) and could not get the largest ones.

3.2 Daily distance

The yearly mean of simulated daily
distances increased with *p* for all
species. For a given *p* value, replicates
gave very close distances (Fig. 3 and
Appendix F Fig. 2). But there was a
larger uncertainty in the observed
yearly mean of daily distances. The
mean of simulated distances was not
significantly different from the mean of
observed distances for a large range of
*p* values. By bootstrapping, the optimal
*p* values were *p* = 70% (range 10–
100%) for pike, *p* = 20% (range 10–
65%) for perch and *p* = 95% (range 50–
100%) for pikeperch (Tab. II). In
general, there was a large overlap of
*p* value ranges between hourly and
daily distances (Tab. II).

Similarly to the hourly distance, the
distribution of simulated daily distances
was less dispersed than that of the
observed ones whatever the *p* value
(Fig. 3 and Appendix F Fig. F2).

3.3 Distance between simulated
and observed density maps

The distance to observed density
maps was generally smaller for the
*3Plake* model simulations than for the
null model (Fig. 4 and Appendix F
using bootstrap. For each value of *p*, we randomly sampled one value of the distance between density maps among the 10 replicates. Then we identified the value of *p* which led to the minimum distance for this sample. We repeated this 1,000 times to get 1,000 values for *p*. For each species, the best value of *p* was the mean across these 1,000 bootstrap values, and the 95% confidence interval was defined by the percentiles 2.5 and 97.5.

Finally, in addition to the density map distance, we mapped the differences between simulated and observed centered and standardized densities for the optimal *p* value of each species to spatially identify them.

# 3 Results

## 3.1 Hourly distance

Yearly mean of simulated hourly distances increased with the selectivity parameter *p*. The three species presented the same general pattern: a steep rise for small values of *p* (up to 5–10%) and then a slight linear increase (Fig. 2 and Appendix F Fig. F1). The simulated distances were very close between the ten replicates (amplitude less than 1 m). But there was a larger uncertainty in the yearly mean of observed hourly distances (i.e., a large confidence interval; Fig. 2). Hence, the mean of simulated distances was not significantly different from the mean of observed distances for a large range of values of *p*. The numerical values obtained by bootstrap for the *p* values giving unbiased estimate of this yearly mean were *p* = 70% (range 10–100%) for pike, *p* = 60% (range 20–100%) for

perch and *p* = 30% (range 10–100%) for pikeperch (Tab. II).

The distribution of simulated hourly distances was less dispersed than that of the observed ones whatever the value of *p* in this range for all species (Fig. 2 and Appendix F Fig. F1). Specifically, simulations captured fewer short distances (null distances in the observations) and could not get the largest ones.

## 3.2 Daily distance

The yearly mean of simulated daily distances increased with *p* for all species. For a given *p* value, replicates gave very close distances (Fig. 3 and Appendix F Fig. 2). But there was a larger uncertainty in the observed yearly mean of daily distances. The mean of simulated distances was not significantly different from the mean of observed distances for a large range of *p* values. By bootstrapping, the optimal *p* values were *p* = 70% (range 10–100%) for pike, *p* = 20% (range 10–65%) for perch and *p* = 95% (range 50–100%) for pikeperch (Tab. II). In general, there was a large overlap of *p* value ranges between hourly and daily distances (Tab. II).

Similarly to the hourly distance, the distribution of simulated daily distances was less dispersed than that of the observed ones whatever the *p* value (Fig. 3 and Appendix F Fig. F2).

## 3.3 Distance between simulated and observed density maps

The distance to observed density maps was generally smaller for the *3Plake* model simulations than for the null model (Fig. 4 and Appendix F
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Yearly mean of simulated hourly distances for perch as a function of the selectivity parameter, *p*, varying from 1% to 100%. The solid red line represents the simulated mean (across all time steps, all individuals and all replicates) and the dashed red lines its range over the ten replicates (very close to the mean). The solid blue line is the yearly mean of observed hourly distances over all data and the dashed blue lines are its 95% confidence interval. Each grey boxplot represents the simulated distribution of the hourly distances for the 365 × 50 × 10 × 24 h. The blue boxplot stands for observed values. The extreme values of the observed boxplot are truncated and the maximum value, 1140, is displayed. Boxplots represent extreme values and 25%, 50% and 75% quantiles.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Yearly mean of simulated hourly distances for perch as a function of the selectivity parameter, *p*, varying from 1% to 100%. The solid red line represents the simulated mean (across all time steps, all individuals and all replicates) and the dashed red lines its range over the ten replicates (very close to the mean). The solid blue line is the yearly mean of observed hourly distances over all data and the dashed blue lines are its 95% confidence interval. Each grey boxplot represents the simulated distribution of the hourly distances for the 365 × 50 × 10 × 24 h. The blue boxplot stands for observed values. The extreme values of the observed boxplot are truncated and the maximum value, 1140, is displayed. Boxplots represent extreme values and 25%, 50% and 75% quantiles.</figcaption></figure>
<table><caption>**Table II.** Parameter *p* (optimal value and range obtained by bootstrap) for which simulation and observation minimized the chosen metrics for each of the following criteria: mean hourly distance, mean daily distance and distance between density maps.</caption><thead><tr><th></th><th>Pike</th><th>Perch</th><th>Pikeperch</th></tr></thead><tbody><tr><td>Hourly distance</td><td>70% (10–100%)</td><td>60% (20–100%)</td><td>30% (10–100%)</td></tr><tr><td>Daily distance</td><td>70% (10–100%)</td><td>20% (10–65%)</td><td>95% (50–100%)</td></tr><tr><td>Density map</td><td>10% (10–10%)</td><td>20% (5–50%)</td><td>61% (25–90%)</td></tr></tbody></table>

Fig. F3), except when *p* > 60% for pike. Based on the bootstrap, the optimal *p* values (minimizing the distance between observed and simulated maps) depended on the species: a smaller value for the pike (10%, range 10–10%), an intermediate value for the perch (20%, range 5–50%) and a higher value for the pikeperch (61%, range 25–90%) (Tab. II). The graphic visualization based on the mean over the 10 replicates (Fig. 4) can differ from the numerical results given by the bootstrap (Tab. II). So, pike seemed to have a spatial distribution linked to quite a high selectivity (low *p*) whereas pikeperch seemed to have a weaker selectivity with respect to HSI. Importantly, the best value identified for *p* for the density map criteria was in the range of the ones identified for hourly and daily distances (Tab. II). In other words, for the best *p* value identified for the density criteria, the yearly means of both hourly and daily distances did not differ significantly from the observed ones.

## 3.4 Density maps

Differences between simulated and observed density maps were displayed for each species, for the optimal value

of the *p* selectivity coefficient (Fig. 5). For the three species, some specific areas of the reservoir revealed higher simulated densities compared to observations. These areas were located mostly in the upstream half of the lake for perch (Fig. 5b) and pikeperch (Fig. 5c), whereas lower densities (compared to observations) were simulated in the downstream part of the lake, especially for perch (Fig. 5b). However, for pike (Fig. 5a) those areas of higher simulated densities (compared to observed ones) were situated in narrow littoral portions both upstream and downstream. Observed density maps are displayed in Appendix E.

# 4 Discussion

## 4.1 Input data

The *3Plake* model needs few and easy to get input data, apart from the HSI maps. In addition, HSI maps are key drivers of this model that assumes that fish movements are mainly driven by the habitat use. Telemetry data are very suitable to compute HSI maps, but they could also be calculated from physical data coupled with available habitat preference models (Laura et al., 2020; Rosenfeld, 2017).
<table><caption>**Table II.** Parameter *p* (optimal value and range obtained by bootstrap) for which simulation and observation minimized the chosen metrics for each of the following criteria: mean hourly distance, mean daily distance and distance between density maps.</caption><thead><tr><th></th><th>Pike</th><th>Perch</th><th>Pikeperch</th></tr></thead><tbody><tr><td>Hourly distance</td><td>70% (10–100%)</td><td>60% (20–100%)</td><td>30% (10–100%)</td></tr><tr><td>Daily distance</td><td>70% (10–100%)</td><td>20% (10–65%)</td><td>95% (50–100%)</td></tr><tr><td>Density map</td><td>10% (10–10%)</td><td>20% (5–50%)</td><td>61% (25–90%)</td></tr></tbody></table>

Fig. F3), except when *p* > 60% for pike. Based on the bootstrap, the optimal *p* values (minimizing the distance between observed and simulated maps) depended on the species: a smaller value for the pike (10%, range 10–10%), an intermediate value for the perch (20%, range 5–50%) and a higher value for the pikeperch (61%, range 25–90%) (Tab. II). The graphic visualization based on the mean over the 10 replicates (Fig. 4) can differ from the numerical results given by the bootstrap (Tab. II). So, pike seemed to have a spatial distribution linked to quite a high selectivity (low *p*) whereas pikeperch seemed to have a weaker selectivity with respect to HSI. Importantly, the best value identified for *p* for the density map criteria was in the range of the ones identified for hourly and daily distances (Tab. II). In other words, for the best *p* value identified for the density criteria, the yearly means of both hourly and daily distances did not differ significantly from the observed ones.

## 3.4 Density maps

Differences between simulated and observed density maps were displayed for each species, for the optimal value

of the *p* selectivity coefficient (Fig. 5). For the three species, some specific areas of the reservoir revealed higher simulated densities compared to observations. These areas were located mostly in the upstream half of the lake for perch (Fig. 5b) and pikeperch (Fig. 5c), whereas lower densities (compared to observations) were simulated in the downstream part of the lake, especially for perch (Fig. 5b). However, for pike (Fig. 5a) those areas of higher simulated densities (compared to observed ones) were situated in narrow littoral portions both upstream and downstream. Observed density maps are displayed in Appendix E.

# 4 Discussion

## 4.1 Input data

The *3Plake* model needs few and easy to get input data, apart from the HSI maps. In addition, HSI maps are key drivers of this model that assumes that fish movements are mainly driven by the habitat use. Telemetry data are very suitable to compute HSI maps, but they could also be calculated from physical data coupled with available habitat preference models (Laura et al., 2020; Rosenfeld, 2017).
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Yearly mean of simulated daily distances for perch as a function of the values of *p* varying from 1% to 100%. The solid red line is the simulated mean (across all time steps, all individuals and all replicates) and the dashed red lines its range over the ten replicates (very close to the mean). The solid blue line is the yearly mean of observed daily distances over all data for Perch, and the dashed blue lines its 95% confidence interval. Each grey boxplot represents the simulated distribution of the daily distances of the 365 × 50 × 10 days. The blue boxplot stands for observed values. The extreme values of the boxplots are truncated and the maximum value is displayed on each boxplot. Boxplots represent extreme values and 25%, 50% and 75% quantiles.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Yearly mean of simulated daily distances for perch as a function of the values of *p* varying from 1% to 100%. The solid red line is the simulated mean (across all time steps, all individuals and all replicates) and the dashed red lines its range over the ten replicates (very close to the mean). The solid blue line is the yearly mean of observed daily distances over all data for Perch, and the dashed blue lines its 95% confidence interval. Each grey boxplot represents the simulated distribution of the daily distances of the 365 × 50 × 10 days. The blue boxplot stands for observed values. The extreme values of the boxplots are truncated and the maximum value is displayed on each boxplot. Boxplots represent extreme values and 25%, 50% and 75% quantiles.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 4. Distance between simulated and observed densities for perch as a function of *p*. The solid blue line is the mean over the 10 replicates and the dashed blue lines the range over the 10 replicates. The null model of this distance is also represented (mean over 1,000 repetitions and 95% confidence interval corresponding to the 2.5 and 97.5 quantiles of these 1,000 values, respectively in green solid line and dashed green lines).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 4. Distance between simulated and observed densities for perch as a function of *p*. The solid blue line is the mean over the 10 replicates and the dashed blue lines the range over the 10 replicates. The null model of this distance is also represented (mean over 1,000 repetitions and 95% confidence interval corresponding to the 2.5 and 97.5 quantiles of these 1,000 values, respectively in green solid line and dashed green lines).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Difference between simulated and observed density maps (unitless) for the optimal *p* value for each species, (a) pike (*p* = 10%), (b) perch (*p* = 20%) and (c) pikeperch (*p* = 61%). Positive values indicate larger density values for simulation than for observation. Negative values indicate smaller density values for simulation than for observation. Values smaller than -4 and larger than 4 are truncated to -4 and 4 on the map. Grey color indicates blind areas where no fish could be tracked by the telemetry system. Upstream part is to the North-East and downstream part to the South-West.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Difference between simulated and observed density maps (unitless) for the optimal *p* value for each species, (a) pike (*p* = 10%), (b) perch (*p* = 20%) and (c) pikeperch (*p* = 61%). Positive values indicate larger density values for simulation than for observation. Negative values indicate smaller density values for simulation than for observation. Values smaller than -4 and larger than 4 are truncated to -4 and 4 on the map. Grey color indicates blind areas where no fish could be tracked by the telemetry system. Upstream part is to the North-East and downstream part to the South-West.</figcaption></figure>
## 4.2 Calibration of the 3Plake model

The calibration of the *3Plake* model is only based on the *p* selectivity coefficient (fraction *p* of the cells with the highest HSI within the hourly distance range). The traveled distance metrics were used to evaluate the range of *p* values whereas, in the end, the density maps were used to choose the *p* value for each species. In the Bariousses reservoir and for each of the three species, the best *p* value evaluated by density maps matched with the range of *p* values according to hourly and daily distances. They were quite different between species with *p* = 10% for pike, *p* = 20% for perch and *p* = 61% for pikeperch. This illustrates that the three fish species showed different degrees of habitat selectivity (higher habitat selectivity for lower *p* values). For pike and perch, the values of *p* that led to the best match between simulated and observed density maps were lower than for pikeperch. This was in agreement with the higher habitat selectivity of pike and perch than pikeperch. It was particularly the case for pike (*p* = 10%, a value given by all 1,000 bootstrap iterations), a species that is known for being strongly attracted to highly structured habitats with vegetation and shallow waters (Inskip, 1982; Kobler *et al.*, 2008). Perch revealed a slightly lower habitat selectivity (*p* = 20%, with a larger range of 5–50). Indeed, even if the species was attracted by littoral and near-littoral areas (Westrelin *et al.*, 2018), it could use the pelagic zone (Jacobsen *et al.*, 2015; Westrelin *et al.*, 2021) and showed a more plastic behaviour (Craig, 2000). Furthermore, it was

shown to adapt its habitat use to minimize interactions with other competitors as pike or pikeperch (Schulze *et al.*, 2006). Pikeperch was the species with the lowest habitat selectivity (*p* = 61%) and with the largest confidence interval for *p* (25–90). Pikeperch frequents deeper waters (Craig, 1987) and the pelagic zone (Vehanen & Lahti, 2003) but can also be found in shallower waters depending on the season (Vehanen & Lahti, 2003; Westrelin *et al.*, 2021). The density maps observed in the Bariousses reservoir confirmed the larger spectrum of habitats used by pikeperch (Appendix E, Fig. E1). Indeed, this species is known to live in a large range of aquatic ecosystems, including those widely anthropized (Kangur *et al.*, 2007; Poulet *et al.*, 2005).

The various degrees of habitat selectivity of species highlight their adaptability to their environment. Pike seemed to be the most specialist species, with the lowest *p* value and *p* value range. As a result, this species is potentially more vulnerable to pressure than perch or pikeperch. Indeed, throughout its native distribution area, in some cases, stocking supports pike populations to compensate environmental degradation (Guillerault *et al.*, 2021; Pierce, 2012). As the reproductive success of pike is very dependent on littoral habitats, the species is particularly vulnerable in reservoirs subject to water level fluctuations (Casselman & Lewis 1996; Crane *et al.*, 2015; Hudon *et al.*, 2010); it can also be impacted by invasive species (Rehage *et al.*, 2016). In contrast, pikeperch revealed a low habitat selectivity (higher *p* value),
## 4.2 Calibration of the 3Plake model

The calibration of the *3Plake* model is only based on the *p* selectivity coefficient (fraction *p* of the cells with the highest HSI within the hourly distance range). The traveled distance metrics were used to evaluate the range of *p* values whereas, in the end, the density maps were used to choose the *p* value for each species. In the Bariousses reservoir and for each of the three species, the best *p* value evaluated by density maps matched with the range of *p* values according to hourly and daily distances. They were quite different between species with *p* = 10% for pike, *p* = 20% for perch and *p* = 61% for pikeperch. This illustrates that the three fish species showed different degrees of habitat selectivity (higher habitat selectivity for lower *p* values). For pike and perch, the values of *p* that led to the best match between simulated and observed density maps were lower than for pikeperch. This was in agreement with the higher habitat selectivity of pike and perch than pikeperch. It was particularly the case for pike (*p* = 10%, a value given by all 1,000 bootstrap iterations), a species that is known for being strongly attracted to highly structured habitats with vegetation and shallow waters (Inskip, 1982; Kobler et al., 2008). Perch revealed a slightly lower habitat selectivity (*p* = 20%, with a larger range of 5–50). Indeed, even if the species was attracted by littoral and near-littoral areas (Westrelin et al., 2018), it could use the pelagic zone (Jacobsen et al., 2015; Westrelin et al., 2021) and showed a more plastic behaviour (Craig, 2000). Furthermore, it was

shown to adapt its habitat use to minimize interactions with other competitors as pike or pikeperch (Schulze et al., 2006). Pikeperch was the species with the lowest habitat selectivity (*p* = 61%) and with the largest confidence interval for *p* (25–90). Pikeperch frequents deeper waters (Craig, 1987) and the pelagic zone (Vehanen & Lahti, 2003) but can also be found in shallower waters depending on the season (Vehanen & Lahti, 2003; Westrelin et al., 2021). The density maps observed in the Bariousses reservoir confirmed the larger spectrum of habitats used by pikeperch (Appendix E, Fig. E1). Indeed, this species is known to live in a large range of aquatic ecosystems, including those widely anthropized (Kangur et al., 2007; Poulet et al., 2005).

The various degrees of habitat selectivity of species highlight their adaptability to their environment. Pike seemed to be the most specialist species, with the lowest *p* value and *p* value range. As a result, this species is potentially more vulnerable to pressure than perch or pikeperch. Indeed, throughout its native distribution area, in some cases, stocking supports pike populations to compensate environmental degradation (Guillerault et al., 2021; Pierce, 2012). As the reproductive success of pike is very dependent on littoral habitats, the species is particularly vulnerable in reservoirs subject to water level fluctuations (Casselman & Lewis 1996; Crane et al., 2015; Hudon et al., 2010); it can also be impacted by invasive species (Rehage et al., 2016). In contrast, pikeperch revealed a low habitat selectivity (higher *p* value),
coherent with its recent increasing abundance in lakes and reservoirs (Huuskonen *et al.*, 2019; Jeppesen *et al.*, 2012) also linked to beneficial effects of climate change on this species (Pekcan-Hekim *et al.*, 2005).

## 4.3 Gaps between observations and simulations

We observed some gaps between observations and simulations on evaluation metrics: the 3Plake model (i) did not represent the longest observed hourly distances, (ii) underestimated the motionless behaviour and (iii) slightly diverged from the observed density map in certain areas depending on species.

Hourly simulated distances were limited by maximum values ($D_{max}$) that hindered the model from simulating the longest observed hourly distances. However, these maximum values were chosen to control the mean distance, and so, to relax this constraint by increasing these maxima would have led to an overestimation of the mean simulated distance compared to the observation. Daily distances were less impacted by this constraint and observed and simulated daily patterns were more in agreement than hourly ones. Even if daily distance is not defined as the numerical addition of 24-hourly segments, it takes into account these 24 simulated hourly movements which could help to simulate daily movements at longer distances, closer to the observed longer daily distances.

Motionless behaviours were also less often simulated than observed, especially in the hourly movements. Independent of HSI, since the probability to

move to a cell was the same for all cells within the maximum distance range, the probability to simulate immobility (fish that stays at the same position) was low compared to the observations. Actually, in the input data, neither HSI nor maximum distance range are informative on this motionless behaviour. To capture this feature could be a further improvement of the model.

The 3Plake model tended to slightly overestimate the densities in the upstream part and some narrow littoral areas of the reservoir at the expense of the downstream part. Except for perch, the highest densities simulated towards the main tributary were in agreement with numerous studies based on observations (e.g., Brosse, 1999; Prchalová et al., 2009; Prchalová et al., 2008; Świerzowski et al., 2000; Vašek et al., 2016), in relation with the riverine origin of the fish fauna and the presence of an upstream-downstream productivity gradient (Vašek et al., 2006). The comparison between observed and simulated density maps (visually and with the Euclidean distance) could have been limited by the fact that, for each species, observed maps were built on a subsample of the species population. The subsamples could have been too small, at least in some seasons, to be representative. This could be enhanced in species showing territorial behaviour as pike (Eklöv, 1992; Skov & Koed, 2004), which moreover had the lowest sample size of ten individuals throughout the whole study, and artificially led to null density zones in the observed map. On the other hand, this could illustrate the complementarity of simulations and observations: by extracting habitat
coherent with its recent increasing abundance in lakes and reservoirs (Huuskonen *et al.*, 2019; Jeppesen *et al.*, 2012) also linked to beneficial effects of climate change on this species (Pekcan-Hekim *et al.*, 2005).

## 4.3 Gaps between observations and simulations

We observed some gaps between observations and simulations on evaluation metrics: the 3Plake model (i) did not represent the longest observed hourly distances, (ii) underestimated the motionless behaviour and (iii) slightly diverged from the observed density map in certain areas depending on species.

Hourly simulated distances were limited by maximum values ($D_{max}$) that hindered the model from simulating the longest observed hourly distances. However, these maximum values were chosen to control the mean distance, and so, to relax this constraint by increasing these maxima would have led to an overestimation of the mean simulated distance compared to the observation. Daily distances were less impacted by this constraint and observed and simulated daily patterns were more in agreement than hourly ones. Even if daily distance is not defined as the numerical addition of 24-hourly segments, it takes into account these 24 simulated hourly movements which could help to simulate daily movements at longer distances, closer to the observed longer daily distances.

Motionless behaviours were also less often simulated than observed, especially in the hourly movements. Independent of HSI, since the probability to

move to a cell was the same for all cells within the maximum distance range, the probability to simulate immobility (fish that stays at the same position) was low compared to the observations. Actually, in the input data, neither HSI nor maximum distance range are informative on this motionless behaviour. To capture this feature could be a further improvement of the model.

The 3Plake model tended to slightly overestimate the densities in the upstream part and some narrow littoral areas of the reservoir at the expense of the downstream part. Except for perch, the highest densities simulated towards the main tributary were in agreement with numerous studies based on observations (e.g., Brosse, 1999; Prchalová et al., 2009; Prchalová et al., 2008; Świerzowski et al., 2000; Vašek et al., 2016), in relation with the riverine origin of the fish fauna and the presence of an upstream-downstream productivity gradient (Vašek et al., 2006). The comparison between observed and simulated density maps (visually and with the Euclidean distance) could have been limited by the fact that, for each species, observed maps were built on a subsample of the species population. The subsamples could have been too small, at least in some seasons, to be representative. This could be enhanced in species showing territorial behaviour as pike (Eklöv, 1992; Skov & Koed, 2004), which moreover had the lowest sample size of ten individuals throughout the whole study, and artificially led to null density zones in the observed map. On the other hand, this could illustrate the complementarity of simulations and observations: by extracting habitat
preferences from a set of individuals
and then applying them to a represen-
tative sample of the population, the
model is able to catch, or at least to give
insight into, features that could not be
handled with few observations.

## 4.4 Perspectives

The *3Plake* model represents quite
well the spatial distribution of three fish
species in the Bariousses reservoir.
However, the model has been
designed to be applied on an annual
scale and, as such, is not appropriate to
focus on the movements during the
spawning period, which constitutes a
zoom on a particular period. Now, a
process of spatial search and selection
of spawning habitat could be added to
the model and activated in the species
adequate periods (Baetens et al., 2013;
Clark et al., 1998; Clark et al., 2008).
Such a model could be used to predict
the effects of habitat restoration or
water level scenarios on the spawning
success of the three fish species.
Similarly to spawning habitat, habitat
restoration would lead to new HSI
maps that could be built with fish
habitat preferences already available
in the reservoir. Comparing model
outputs from simulations based on
these restored maps with simulations
run with non-restored ones would give
insights into theoretical impacts of the
restoration on spawning success. The
same approach could be applied to
compare different WLF scenarios.

5 Conclusions

The *3Plake* model, calibrated using
high resolution data, is only based on
HSI maps and maximum hourly distan-
ces. Though quite simple, it simulated
some interesting patterns of movement
and habitat selection as discussed
above. It is generic and could be applied
to many other fish species as long as HSI
maps and hourly distance range are
available at each time step. It implicitly
integrates some biotic interactions
among individuals or among fish spe-
cies, because model inputs are based
on high resolution observations when all
three species coexisted in the reservoir.
Other biotic factors, such as prey
distribution in the reservoir, are also
implicitly partly integrated into HSI
maps, because in relation with physical
habitat.

The *3Plake* spatially explicit IBM
simulated yearly mean hourly and daily
distances for pike, perch and pike-
perch. Specific values of the selectivity
parameter made it possible to simulate
fish spatial densities close enough to
the observed ones to be informative
and consistent with the available habi-
tat. A next step will be the addition of a
spawning module to predict the impact
of habitat restoration or water level
management on the population dynam-
ics. This final model could be a useful
tool for stakeholders, to get insights into
the effects of environmental measures
on fish densities and fish spawning
success.
preferences from a set of individuals
and then applying them to a represen-
tative sample of the population, the
model is able to catch, or at least to give
insight into, features that could not be
handled with few observations.

## 4.4 Perspectives

The *3Plake* model represents quite
well the spatial distribution of three fish
species in the Bariousses reservoir.
However, the model has been
designed to be applied on an annual
scale and, as such, is not appropriate to
focus on the movements during the
spawning period, which constitutes a
zoom on a particular period. Now, a
process of spatial search and selection
of spawning habitat could be added to
the model and activated in the species
adequate periods (Baetens et al., 2013;
Clark et al., 1998; Clark et al., 2008).
Such a model could be used to predict
the effects of habitat restoration or
water level scenarios on the spawning
success of the three fish species.
Similarly to spawning habitat, habitat
restoration would lead to new HSI
maps that could be built with fish
habitat preferences already available
in the reservoir. Comparing model
outputs from simulations based on
these restored maps with simulations
run with non-restored ones would give
insights into theoretical impacts of the
restoration on spawning success. The
same approach could be applied to
compare different WLF scenarios.

5 Conclusions

The *3Plake* model, calibrated using
high resolution data, is only based on
HSI maps and maximum hourly distan-
ces. Though quite simple, it simulated
some interesting patterns of movement
and habitat selection as discussed
above. It is generic and could be applied
to many other fish species as long as HSI
maps and hourly distance range are
available at each time step. It implicitly
integrates some biotic interactions
among individuals or among fish spe-
cies, because model inputs are based
on high resolution observations when all
three species coexisted in the reservoir.
Other biotic factors, such as prey
distribution in the reservoir, are also
implicitly partly integrated into HSI
maps, because in relation with physical
habitat.

The *3Plake* spatially explicit IBM
simulated yearly mean hourly and daily
distances for pike, perch and pike-
perch. Specific values of the selectivity
parameter made it possible to simulate
fish spatial densities close enough to
the observed ones to be informative
and consistent with the available habi-
tat. A next step will be the addition of a
spawning module to predict the impact
of habitat restoration or water level
management on the population dynam-
ics. This final model could be a useful
tool for stakeholders, to get insights into
the effects of environmental measures
on fish densities and fish spawning
success.
<table><caption>Table B.1 Mean (range) of tracking days, number of positions, total length and weight are given over the pool of individuals by species.</caption><thead><tr><th>Species</th><th>Number of individuals</th><th>Number of tracking days</th><th>Number of recorded positions per individual</th><th>Total length (mm)</th><th>Weight (g)</th></tr></thead><tbody><tr><td>Pike</td><td>10</td><td>122<br>(8-420)</td><td>15759<br>(128-55394)</td><td>532<br>(425-629)</td><td>958<br>(398-1513)</td></tr><tr><td>Perch</td><td>22</td><td>182<br>(16-442)</td><td>38245<br>(2392-117621)</td><td>402<br>(320-486)</td><td>956<br>(383-1800)</td></tr><tr><td>Pikeperch</td><td>26</td><td>126<br>(5-442)</td><td>31102<br>(818-125551)</td><td>494<br>(360-695)</td><td>1136<br>(354-3000)</td></tr></tbody></table>

## Fundings

This work was supported by HYNES (INRAE – EDF R&D) program.

## Supplementary material

An individual-based model to predict the spatial distribution of northern pike, European perch and pikeperch from habitat use in a reservoir subject to water level fluctuations. Un modèle individu-centré pour prédire la distribution spatiale du brochet, de la perche commune et du sandre, à partir de l'utilisation de l'habitat dans un réservoir soumis à des fluctuations de niveau d'eau.

## Appendix A: Water level fluctuation

**Figure A1.** Water level time series over the tracking period (March 2012 - March 2014). Data are hourly and their vertical resolution is 0.1 m. The horizontal red arrow indicates the yearly time series used for the simulation: year 2013.

## Appendix B: Characteristics of tracked individuals of pike, perch and pikeperch

## Appendix C: HSI calculation

For each of the three species, a selection ratio (SR) was calculated for five habitat variables, independently for each season, following the method of Westrelin et al. (2018) for perch. The first variable was water depth (divided in 7 depth classes for SR calculation: [0; 2.5[, [2.5; 5[, [5; 7.5[, [7.5; 10[, [10; 12.5[, [12.5;15[, [15; 22[ m). It was available for the whole lake and was updated at each time step with the water level time series. The four other variables were main substrate (silt, sand, gravel, pebble, stone, boulder, rock and lawn which made a particular class as no other vegetation was observed except these flooded lawns), overhanging vegetation (yes, no), tree stumps (yes, no) and emerging trees (yes, no) in the littoral zone. These last ones were only available in the littoral zone.
<table><caption>Table B.1 Mean (range) of tracking days, number of positions, total length and weight are given over the pool of individuals by species.</caption><thead><tr><th>Species</th><th>Number of individuals</th><th>Number of tracking days</th><th>Number of recorded positions per individual</th><th>Total length (mm)</th><th>Weight (g)</th></tr></thead><tbody><tr><td>Pike</td><td>10</td><td>122<br>(8-420)</td><td>15759<br>(128-55394)</td><td>532<br>(425-629)</td><td>958<br>(398-1513)</td></tr><tr><td>Perch</td><td>22</td><td>182<br>(16-442)</td><td>38245<br>(2392-117621)</td><td>402<br>(320-486)</td><td>956<br>(383-1800)</td></tr><tr><td>Pikeperch</td><td>26</td><td>126<br>(5-442)</td><td>31102<br>(818-125551)</td><td>494<br>(360-695)</td><td>1136<br>(354-3000)</td></tr></tbody></table>

## Fundings

This work was supported by HYNES (INRAE – EDF R&D) program.

## Supplementary material

An individual-based model to predict the spatial distribution of northern pike, European perch and pikeperch from habitat use in a reservoir subject to water level fluctuations. Un modèle individu-centré pour prédire la distribution spatiale du brochet, de la perche commune et du sandre, à partir de l'utilisation de l'habitat dans un réservoir soumis à des fluctuations de niveau d'eau.

## Appendix A: Water level fluctuation

**Figure A1.** Water level time series over the tracking period (March 2012 - March 2014). Data are hourly and their vertical resolution is 0.1 m. The horizontal red arrow indicates the yearly time series used for the simulation: year 2013.

## Appendix B: Characteristics of tracked individuals of pike, perch and pikeperch

## Appendix C: HSI calculation

For each of the three species, a selection ratio (SR) was calculated for five habitat variables, independently for each season, following the method of Westrelin et al. (2018) for perch. The first variable was water depth (divided in 7 depth classes for SR calculation: [0; 2.5[, [2.5; 5[, [5; 7.5[, [7.5; 10[, [10; 12.5[, [12.5;15[, [15; 22[ m). It was available for the whole lake and was updated at each time step with the water level time series. The four other variables were main substrate (silt, sand, gravel, pebble, stone, boulder, rock and lawn which made a particular class as no other vegetation was observed except these flooded lawns), overhanging vegetation (yes, no), tree stumps (yes, no) and emerging trees (yes, no) in the littoral zone. These last ones were only available in the littoral zone.
<table><caption>Table D.1 Maximum hourly distance (Ra in m) for each species, season and phase of the day</caption><thead><tr><th rowspan="2">Species</th><th rowspan="2">Season</th><th colspan="4">Phase of the day</th></tr><tr><th>Dawn</th><th>Day</th><th>Dusk</th><th>Night</th></tr></thead><tbody><tr><td rowspan="4">Pike</td><td>Spring</td><td>204</td><td>233</td><td>199</td><td>53</td></tr><tr><td>Summer</td><td>153</td><td>201</td><td>94</td><td>31</td></tr><tr><td>Autumn</td><td>151</td><td>131</td><td>117</td><td>29</td></tr><tr><td>Winter</td><td>172</td><td>133</td><td>170</td><td>78</td></tr><tr><td rowspan="4">Perch</td><td>Spring</td><td>177</td><td>175</td><td>99</td><td>15</td></tr><tr><td>Summer</td><td>235</td><td>308</td><td>156</td><td>27</td></tr><tr><td>Autumn</td><td>170</td><td>248</td><td>117</td><td>15</td></tr><tr><td>Winter</td><td>128</td><td>148</td><td>105</td><td>10</td></tr><tr><td rowspan="4">Pikeperch</td><td>Spring</td><td>60</td><td>36</td><td>58</td><td>53</td></tr><tr><td>Summer</td><td>206</td><td>92</td><td>184</td><td>170</td></tr><tr><td>Autumn</td><td>134</td><td>56</td><td>148</td><td>136</td></tr><tr><td>Winter</td><td>126</td><td>44</td><td>94</td><td>77</td></tr></tbody></table>

The habitat suitability index (HSI) was calculated for each 10 m x 10 m cell and each time step as the sum of the local SR for each variable divided by the sum of the maximum SR for each variable. For the littoral area, the HSI was calculated with the five variables. For the center of the lake, the HSI was calculated with the water depth variable only.

In order to have a synthetic visual representation of HSI data, the HSI was averaged over the year (Figure C1) and in each season (Figure C2 for pike, Figure B3 for perch and Figure C4 for pikeperch) with the water level time series used for simulations.

**Figure C1.** HSI map (averaged along the year with the water level time series used for simulations) for (a) pike; (b) perch; (c) pikeperch. Color legend: from white, low HSI, to red, high HSI.

**Figure C2.** HSI map (averaged along the days of a season with the water level time series used for simulations) for pike during (a) spring, (b) summer, (c) autumn, (d) winter. Color legend: from white, low HSI, to red, high HSI.

**Figure C3.** HSI map (averaged along the days of a season with the water level time series used for simulations) for perch during (a) spring, (b)

summer, (c) autumn, (d) winter. Color legend: from white, low HSI, to red, high HSI.

**Figure C4.** HSI map (averaged along the days of a season with the water level time series used for simulations) for pikeperch during (a) spring, (b) summer, (c) autumn, (d) winter. Color legend: from white, low HSI, to red, high HSI.

## Appendix D: Hourly distance range

## Appendix E: Observed fish density map

**Figure E1.** Observed density map for (a) pike, (b) perch and (c) pikeperch. Color legend: from white, low densities, to red, high densities. The log of the mean yearly density is represented. The grey areas are blind areas where no fish could be detected by the tracking system.

## Appendix F: Supplementary figures

**Figure F1.** Mean simulated hourly distance for (a) pike and (b) pikeperch over a year as a function of p parameter varying from 1 to 100 %. The solid red line is the simulated mean (across all time steps, all individuals and all replicates)
<table><caption>Table D.1 Maximum hourly distance (Ra in m) for each species, season and phase of the day</caption><thead><tr><th rowspan="2">Species</th><th rowspan="2">Season</th><th colspan="4">Phase of the day</th></tr><tr><th>Dawn</th><th>Day</th><th>Dusk</th><th>Night</th></tr></thead><tbody><tr><td rowspan="4">Pike</td><td>Spring</td><td>204</td><td>233</td><td>199</td><td>53</td></tr><tr><td>Summer</td><td>153</td><td>201</td><td>94</td><td>31</td></tr><tr><td>Autumn</td><td>151</td><td>131</td><td>117</td><td>29</td></tr><tr><td>Winter</td><td>172</td><td>133</td><td>170</td><td>78</td></tr><tr><td rowspan="4">Perch</td><td>Spring</td><td>177</td><td>175</td><td>99</td><td>15</td></tr><tr><td>Summer</td><td>235</td><td>308</td><td>156</td><td>27</td></tr><tr><td>Autumn</td><td>170</td><td>248</td><td>117</td><td>15</td></tr><tr><td>Winter</td><td>128</td><td>148</td><td>105</td><td>10</td></tr><tr><td rowspan="4">Pikeperch</td><td>Spring</td><td>60</td><td>36</td><td>58</td><td>53</td></tr><tr><td>Summer</td><td>206</td><td>92</td><td>184</td><td>170</td></tr><tr><td>Autumn</td><td>134</td><td>56</td><td>148</td><td>136</td></tr><tr><td>Winter</td><td>126</td><td>44</td><td>94</td><td>77</td></tr></tbody></table>

The habitat suitability index (HSI) was calculated for each 10 m x 10 m cell and each time step as the sum of the local SR for each variable divided by the sum of the maximum SR for each variable. For the littoral area, the HSI was calculated with the five variables. For the center of the lake, the HSI was calculated with the water depth variable only.

In order to have a synthetic visual representation of HSI data, the HSI was averaged over the year (Figure C1) and in each season (Figure C2 for pike, Figure B3 for perch and Figure C4 for pikeperch) with the water level time series used for simulations.

**Figure C1.** HSI map (averaged along the year with the water level time series used for simulations) for (a) pike; (b) perch; (c) pikeperch. Color legend: from white, low HSI, to red, high HSI.

**Figure C2.** HSI map (averaged along the days of a season with the water level time series used for simulations) for pike during (a) spring, (b) summer, (c) autumn, (d) winter. Color legend: from white, low HSI, to red, high HSI.

**Figure C3.** HSI map (averaged along the days of a season with the water level time series used for simulations) for perch during (a) spring, (b)

summer, (c) autumn, (d) winter. Color legend: from white, low HSI, to red, high HSI.

**Figure C4.** HSI map (averaged along the days of a season with the water level time series used for simulations) for pikeperch during (a) spring, (b) summer, (c) autumn, (d) winter. Color legend: from white, low HSI, to red, high HSI.

## Appendix D: Hourly distance range

## Appendix E: Observed fish density map

**Figure E1.** Observed density map for (a) pike, (b) perch and (c) pikeperch. Color legend: from white, low densities, to red, high densities. The log of the mean yearly density is represented. The grey areas are blind areas where no fish could be detected by the tracking system.

## Appendix F: Supplementary figures

**Figure F1.** Mean simulated hourly distance for (a) pike and (b) pikeperch over a year as a function of p parameter varying from 1 to 100 %. The solid red line is the simulated mean (across all time steps, all individuals and all replicates)
and the dashed ones are its range over the ten replicates (very close to the mean). The solid blue line is the mean observed hourly distance over all data and the dashed ones are its 95% confidence interval. Each grey boxplot represents the simulated distribution of the hourly distances for the 365x50x10x24 hours. The blue boxplot stands for observed values. The extreme values of the observed boxplot are truncated and the maximum value is displayed.

**Figure F2.** Mean simulated daily distance for (a) pike and (b) pikeperch as a function of the values of p varying from 1 to 100 %. The solid red line is the simulated mean (across all time steps, all individuals and all replicates) and the dashed ones its range over the ten replicates. The solid blue line is the mean observed daily distance over all data for each species, and the dashed ones its 95% confidence interval. Each grey boxplot represents the simulated distribution of the daily distances of the 365x50x10 days. The blue boxplot stands for observed values. The extreme values of the boxplots are truncated and the maximum value is displayed on each boxplot.

**Figure F3.** Distance between simulated and observed densities (mean and range) for (a) pike and (b) pikeperch as a function of p. The solid blue line is the average over the 10 replicates and the dashed blue lines are the range over the 10 replicates. The null model of this distance is also represented (mean and 95% confidence interval over 1,000 repetitions, respectively in green solid line and dashed green lines).

## ACKNOWLEDGEMENTS

We would like to thank Guillaume Garbay who developed a first version of the model during his Master internship Nicolas Dumoulin who developed SimAquaLife and co-supervised this Master Gérard Henry who helped in transferring the model to Linux and Coralie Garron for a careful reading and English corrections. And we sincerely thank the two reviewers for their valuable comments that helped to improve the manuscript.

## REFERENCES

Baetens J.M., Van Nieuland S., Pauwels I.S., De Baets B., Mouton A.M. & Goethals P.L.M., 2013. An individual-based model for the migration of pike (*Esox lucius*) in the river Yser, Belgium. *Ecol Modell* 258: 40–52.

Baktoft H., Aarestrup K., Berg S., Boel M., Jacobsen L., Jepsen N., Koed A., Svendsen J.C. & Skov C., 2012. Seasonal and diel effects on the activity of northern pike studied by high-resolution positional telemetry. *Ecol Freshw Fish* 21 (3): 386–394.

Beaumont W., Hodder K., Masters J., Scott L. & Welton J., 2005. Activity patterns in pike (*Esox lucius*), as determined by motion-sensing telemetry. Aquat Telemetry: Adv Appl. Rome: FAO/COISPA: 231–243.

Blanchard J.R., Santos R.O. & Rehage J.S., 2021. Sociability interacts with temporal environmental variation to spatially structure metapopulations: a fish dispersal simulation in an ephemeral landscape. *Ecol Modell* 443: 109458.

Brosse S., 1999. Habitat, dynamique spatiale et structure des communautés pisciaires en milieu lacustre – Etude de la retenue de Pareloup (Aveyron, France). 203 pp.

Carmignani J.R. & Roy A.H., 2017. Ecological impacts of winter water level drawdowns on lake littoral zones: a review. *Aquat Sci* 79 (4): 803–824.

Casselman J. & Lewis C.A., 1996. Habitat requirements of northern pike (*Esox lucius*). *Can J Fish Aquat Sci* 53: 161–174.

Chapman C. & Mackay W., 1984. Direct observation of habitat utilization by Northern Pike. *Copeia* 1984: 255.

Chapman C. & Mackay W.C., 1984. Versatility in habitat use by a top aquatic predator, *Esox lucius* L. *J Fish Biol* 25: 109–115.

Clark M., Rose K., Chandler J., Richter T., Orth D. & Winkle W., 1998. Simulating smallmouth bass reproductive success in reservoirs. *Environ Biol Fishes* 51: 161–174.

Clark M.E., Rose K.A., Chandler J.A., Richter T. J., Orth D.J. & Van Winkle W., 2008. Water-level fluctuation effects on centrarchid repro-
and the dashed ones are its range over the ten replicates (very close to the mean). The solid blue line is the mean observed hourly distance over all data and the dashed ones are its 95% confidence interval. Each grey boxplot represents the simulated distribution of the hourly distances for the 365x50x10x24 hours. The blue boxplot stands for observed values. The extreme values of the observed boxplot are truncated and the maximum value is displayed.

**Figure F2.** Mean simulated daily distance for (a) pike and (b) pikeperch as a function of the values of p varying from 1 to 100 %. The solid red line is the simulated mean (across all time steps, all individuals and all replicates) and the dashed ones its range over the ten replicates. The solid blue line is the mean observed daily distance over all data for each species, and the dashed ones its 95% confidence interval. Each grey boxplot represents the simulated distribution of the daily distances of the 365x50x10 days. The blue boxplot stands for observed values. The extreme values of the boxplots are truncated and the maximum value is displayed on each boxplot.

**Figure F3.** Distance between simulated and observed densities (mean and range) for (a) pike and (b) pikeperch as a function of p. The solid blue line is the average over the 10 replicates and the dashed blue lines are the range over the 10 replicates. The null model of this distance is also represented (mean and 95% confidence interval over 1,000 repetitions, respectively in green solid line and dashed green lines).

## ACKNOWLEDGEMENTS

We would like to thank Guillaume Garbay who developed a first version of the model during his Master internship Nicolas Dumoulin who developed SimAquaLife and co-supervised this Master Gérard Henry who helped in transferring the model to Linux and Coralie Garron for a careful reading and English corrections. And we sincerely thank the two reviewers for their valuable comments that helped to improve the manuscript.

## REFERENCES

Baetens J.M., Van Nieuland S., Pauwels I.S., De Baets B., Mouton A.M. & Goethals P.L.M., 2013. An individual-based model for the migration of pike (*Esox lucius*) in the river Yser, Belgium. *Ecol Modell* 258: 40–52.

Baktoft H., Aarestrup K., Berg S., Boel M., Jacobsen L., Jepsen N., Koed A., Svendsen J.C. & Skov C., 2012. Seasonal and diel effects on the activity of northern pike studied by high-resolution positional telemetry. *Ecol Freshw Fish* 21 (3): 386–394.

Beaumont W., Hodder K., Masters J., Scott L. & Welton J., 2005. Activity patterns in pike (*Esox lucius*), as determined by motion-sensing telemetry. Aquat Telemetry: Adv Appl. Rome: FAO/COISPA: 231–243.

Blanchard J.R., Santos R.O. & Rehage J.S., 2021. Sociability interacts with temporal environmental variation to spatially structure metapopulations: a fish dispersal simulation in an ephemeral landscape. *Ecol Modell* 443: 109458.

Brosse S., 1999. Habitat, dynamique spatiale et structure des communautés pisciaires en milieu lacustre – Etude de la retenue de Pareloup (Aveyron, France). 203 pp.

Carmignani J.R. & Roy A.H., 2017. Ecological impacts of winter water level drawdowns on lake littoral zones: a review. *Aquat Sci* 79 (4): 803–824.

Casselman J. & Lewis C.A., 1996. Habitat requirements of northern pike (*Esox lucius*). *Can J Fish Aquat Sci* 53: 161–174.

Chapman C. & Mackay W., 1984. Direct observation of habitat utilization by Northern Pike. *Copeia* 1984: 255.

Chapman C. & Mackay W.C., 1984. Versatility in habitat use by a top aquatic predator, *Esox lucius* L. *J Fish Biol* 25: 109–115.

Clark M., Rose K., Chandler J., Richter T., Orth D. & Winkle W., 1998. Simulating smallmouth bass reproductive success in reservoirs. *Environ Biol Fishes* 51: 161–174.

Clark M.E., Rose K.A., Chandler J.A., Richter T. J., Orth D.J. & Van Winkle W., 2008. Water-level fluctuation effects on centrarchid repro-
ductive success in reservoirs: a modeling
analysis. *North Am J Fish Manag* 28 (4):
1138–1156.

Cook M.F. & Bergersen E.P., 1988. Movements, habitat selection, and activity periods of Northern Pike in Eleven Mile Reservoir, Colorado. *Trans Am Fish Soc* 117 (5): 495–502.

Cott P.A., Sibley P.K., Somers W.M., Lilly M.R. & Gordon A.M., 2008. A review of water level fluctuations on aquatic biota with an emphasis on fishes in ice-covered lakes. *J Am Water Resour Assoc* 44 (2): 343–359.

Craig J., 1996. *Pike: Biology and Exploitation*. Springer Netherlands.

Craig J., 2000. *Percid Fishes: Systematics, Ecology and Exploitation*. *Wiley*, 370 pp.

Craig J.F., 1987. *The Biology of Perch and Related Fish*. London: Croom Helm.

Craig J.F., 2008. *A Short Review of Pike Ecology*. *Hydrobiologia* 601 (1): 5–16.

Crane D.P., Miller L.M., Diana J.S., Casselman J.
M., Farrell J.M., Kapuscinski K.L. & Nohner J.K.,
2015. Muskellunge and northern pike ecology
and management: important issues and re-
search needs. *Fisheries* 40 (6): 258–267.

de Moraes K.R., Souza A.T., Muška M., Hladík M., Čtvrtlíková M., Draštík V., Kolarík T., Kučerová A., Krolová M., Sajdlová Z., Šmejkal M. & Kubečka J., 2023. Artificial floating islands: a promising tool to support juvenile fish in lacustrine systems. *Hydrobiologia* 850 (9): 1969–1984.

Dumoulin N., 2007. *SimAquaLife: a framework for aquatic organisms spatial dynamics modelling*. Revue des Sciences et Technologies de l'Information – Série TSI : Technique et Science Informatiques, 26 (6): 701–721.

Eklöv P., 1992. Group foraging *versus* solitary foraging efficiency in piscivorous predators: the perch, *Perca fluviatilis*, and pike, *Esox lucius*, patterns. *Anim Behav* 44: 313–326.

Evtimova V. & Donohue I., 2014. Quantifying
ecological responses to amplified water level
fluctuations in standing waters: an experi-
mental approach. *J Appl Ecol* 51.

Farò D., Zolezzi G. & Wolter C., 2021. How much habitat does a river need? A spatially-explicit population dynamics model to assess ratios of ontogenetical habitat needs. *J Environ Manag* 286: 112100.

Gasith A. & Gafny S., 1998. Importance of
physical structures in lakes: the case of lake
kinneret and general implications. In: *The
Structuring Role of Submerged Macrophytes
in Lakes* (E. Jeppesen, M. Søndergaard, M.
Søndergaard, K. Christoffersen Eds.) pp.
331–338.

Grimm V., Berger U., Bastiansen F., Eliassen S., Ginot V., Giske J., Goss-Custard J., Grand T., Heinz S.K., Huse G., Huth A., Jepsen J.U., Jørgensen C., Mooij W.M., Müller B., Pe'er G., Piou C., Railsback S.F., Robbins A.M., Robbins M.M., Rossmanith E., Rüger N., Strand E., Souissi S., Stillman R.A., Vabø R., Visser U. & DeAngelis D.L., 2006. A standard protocol for describing individual-based and agent-based models. *Ecol Modell* 198 (1): 115–126.

Grimm V., Berger U., DeAngelis D.L., Polhill J.G., Giske J. & Railsback S.F., 2010. The ODD protocol: a review and first update. *Ecol Modell* 221 (23): 2760–2768.

Grimm V., Railsback S.F., Vincenot C.E., Berger U., Gallagher C., DeAngelis D.L., Edmonds B., Ge J., Giske J., Groeneveld J., uuml, rgen, Johnston A.S.A., Milles A., Nabe-Nielsen J., Polhill J.G., Radchuk V., Rohw, uuml, der M.-S., Stillman R. A., Thiele J. C., Ayll, oacute D. n., 2020. The ODD protocol for describing agent-based and other simulation models: a second update to improve clarity, replication, and structural realism. *J Artif Soc Soc Simul* 23 (2): 7.

Guillierault N., Loot G., Blanchet S., Millet P., Musseau C., Cucherousset J. & Santoul F., 2021. Efficiency of Northern pike (*Esox lucius*) stocking in metropolitan France at large spatial and temporal scales. *Fish Manag Ecol* 28 (5): 486–495.

Halleraker J.H., van de Bund F W., Bussettini M., Gosling R., Döbbelt-Grüne S., Hensman J., Kling J., Koller-Kreimel V. & Pollard P., 2016. Working group ECOSTAT report on common understanding of using mitigation measures for reaching good ecological potential for heavily modified water bodies. 104.

Hayes D., Jones M., Lester N., Chu C., Doka S., Netto J., Stockwell J., Thompson B., Minns C., Shuter B. & Collins N., 2009. Linking fish population dynamics to habitat conditions: insights from the application of a process-oriented approach to several Great Lakes species. *Rev Fish Biol Fish* 19 (3): 295–312.
ductive success in reservoirs: a modeling analysis. *North Am J Fish Manag* 28 (4): 1138–1156.

Cook M.F. & Bergersen E.P., 1988. Movements, habitat selection, and activity periods of Northern Pike in Eleven Mile Reservoir, Colorado. *Trans Am Fish Soc* 117 (5): 495–502.

Cott P.A., Sibley P.K., Somers W.M., Lilly M.R. & Gordon A.M., 2008. A review of water level fluctuations on aquatic biota with an emphasis on fishes in ice-covered lakes. *J Am Water Resour Assoc* 44 (2): 343–359.

Craig J., 1996. *Pike: Biology and Exploitation*. Springer Netherlands.

Craig J., 2000. *Percid Fishes: Systematics, Ecology and Exploitation*. *Wiley*, 370 pp.

Craig J.F., 1987. *The Biology of Perch and Related Fish*. London: Croom Helm.

Craig J.F., 2008. A Short Review of Pike Ecology. *Hydrobiologia* 601 (1): 5–16.

Crane D.P., Miller L.M., Diana J.S., Casselman J. M., Farrell J.M., Kapuscinski K.L. & Nohner J.K., 2015. Muskellunge and northern pike ecology and management: important issues and research needs. *Fisheries* 40 (6): 258–267.

de Moraes K.R., Souza A.T., Muška M., Hladík M., Čtvrtlíková M., Draštík V., Kolarík T., Kučerová A., Krolová M., Sajdlová Z., Šmejkal M. & Kubečka J., 2023. Artificial floating islands: a promising tool to support juvenile fish in lacustrine systems. *Hydrobiologia* 850 (9): 1969–1984.

Dumoulin N., 2007. *SimAquaLife: a framework for aquatic organisms spatial dynamics modelling*. Revue des Sciences et Technologies de l'Information – Série TSI : Technique et Science Informatiques, 26 (6): 701–721.

Eklöv P., 1992. Group foraging *versus* solitary foraging efficiency in piscivorous predators: the perch, *Perca fluviatilis*, and pike, *Esox lucius*, patterns. *Anim Behav* 44: 313–326.

Evtimova V. & Donohue I., 2014. Quantifying ecological responses to amplified water level fluctuations in standing waters: an experimental approach. *J Appl Ecol* 51.

Farò D., Zolezzi G. & Wolter C., 2021. How much habitat does a river need? A spatially-explicit population dynamics model to assess ratios of ontogenetical habitat needs. *J Environ Manag* 286: 112100.

Gasith A. & Gafny S., 1998. Importance of physical structures in lakes: the case of lake kinneret and general implications. In: *The Structuring Role of Submerged Macrophytes in Lakes* (E. Jeppesen, M. Søndergaard, M. Søndergaard, K. Christoffersen Eds.) pp. 331–338.

Grimm V., Berger U., Bastiansen F., Eliassen S., Ginot V., Giske J., Goss-Custard J., Grand T., Heinz S.K., Huse G., Huth A., Jepsen J.U., Jørgensen C., Mooij W.M., Müller B., Pe'er G., Piou C., Railsback S.F., Robbins A.M., Robbins M.M., Rossmanith E., Rüger N., Strand E., Souissi S., Stillman R.A., Vabø R., Visser U. & DeAngelis D.L., 2006. A standard protocol for describing individual-based and agent-based models. *Ecol Modell* 198 (1): 115–126.

Grimm V., Berger U., DeAngelis D.L., Polhill J.G., Giske J. & Railsback S.F., 2010. The ODD protocol: a review and first update. *Ecol Modell* 221 (23): 2760–2768.

Grimm V., Railsback S.F., Vincenot C.E., Berger U., Gallagher C., DeAngelis D.L., Edmonds B., Ge J., Giske J., Groeneveld J., uuml, rgen, Johnston A.S.A., Milles A., Nabe-Nielsen J., Polhill J.G., Radchuk V., Rohw, uuml, der M.-S., Stillman R. A., Thiele J. C., Ayll, oacute D. n., 2020. The ODD protocol for describing agent-based and other simulation models: a second update to improve clarity, replication, and structural realism. *J Artif Soc Soc Simul* 23 (2): 7.

Guillerrault N., Loot G., Blanchet S., Millet P., Musseau C., Cucherousset J. & Santoul F., 2021. Efficiency of Northern pike (*Esox lucius*) stocking in metropolitan France at large spatial and temporal scales. *Fish Manag Ecol* 28 (5): 486–495.

Halleraker J.H., van de Bund F W., Bussettini M., Gosling R., Döbbelt-Grüne S., Hensman J., Kling J., Koller-Kreimel V. & Pollard P., 2016. Working group ECOSTAT report on common understanding of using mitigation measures for reaching good ecological potential for heavily modified water bodies. 104.

Hayes D., Jones M., Lester N., Chu C., Doka S., Netto J., Stockwell J., Thompson B., Minns C., Shuter B. & Collins N., 2009. Linking fish population dynamics to habitat conditions: insights from the application of a process-oriented approach to several Great Lakes species. *Rev Fish Biol Fish* 19 (3): 295–312.
Hirsch P.E., Eloranta A.P., Amundsen P.-A., Brabrand Å., Charmasson J., Helland I.P., Power M., Sánchez-Hernández J., Sandlund O.T., Sauterleute J.F., Skoglund S., Ugedal O. & Yang H., 2017. Effects of water level regulation in alpine hydropower reservoirs: an ecosystem perspective with a special emphasis on fish. *Hydrobiologia* 794 (1): 287–301.

Hjorth P. & Bengtsson L., 2012. Large dams, statistics and critical review. In: *Encyclopedia of Lakes and Reservoirs* (L. Bengtsson, R.W. Herschy, R.W. Fairbridge Eds.) pp. 475–479.

Horký P., Slavík O. & Bartoš L., 2008. A telemetry study on the diurnal distribution and activity of adult pikeperch, *Sander lucioperca* (L.), in a riverine environment. *Hydrobiologia* 614 (1): 151.

Hovel K.A. & Regan H.M., 2008. Using an individual-based model to examine the roles of habitat fragmentation and behavior on predator-prey relationships in seagrass landscapes. *Landsc Ecol* 23 (1): 75–89.

Hudon C., Armellin A., Gagnon P. & Patoine A., 2010. Variations in water temperatures and levels in the St. Lawrence River (Québec, Canada) and potential implications for three common fish species. *Hydrobiologia* 647 (1): 145–161.

Hudon C., Gagnon P., Amyot J.-P., Létourneau G., Jean M., Plante C., Rioux D. & Deschênes M., 2005. Historical changes in herbaceous wetland distribution induced by hydrological conditions in Lake Saint-Pierre (St. Lawrence River, Quebec, Canada). *Hydrobiologia* 539 (1): 205–224.

Huuskonen H., Piironen J., Syväranta J., Eronen R., Biasi C., Kiiskinen P., Kortet R. & Vainikka A., 2019. Diet and movements of pikeperch (*Sander lucioperca*) in a large oligotrophic lake with an exceptionally high pikeperch yield. *Ecol Freshw Fish* 28 (4): 533–543.

Imbrock F., Appenzeller A. & Eckmann R., 2005. Diel and seasonal distribution of perch in Lake Constance: a hydroacoustic study and *in situ* observations. *J Fish Biol* 49: 1–13.

Inskip P.D., 1982. Habitat suitability index models: northern pike. In: FWS/OBS.

Jacobsen L., Berg S., Baktoft H. & Skov C., 2015. Behavioural strategy of large perch *Perca fluviatilis* varies between a mesotrophic and a hypereutrophic lake. *J Fish Biol* 86 (3): 1016–1029.

Jeppesen E., Mehner T., Winfield I.J., Kangur K., Sarvala J., Gerdeaux D., Rask M., Malmquist H.J., Holmgren K. & Volta P., 2012. Impacts of climate warming on the long-term dynamics of key fish species in 24 European lakes. *Hydrobiologia* 694 (1): 1–39.

Kaczka L.J. & Miranda L.E., 2014. Size of age-0 crappies (*Pomoxis* spp.) relative to reservoir habitats and water levels. *J Freshw Ecol* 29 (4): 525–534.

Kangur K., Park Y.-S., Kangur A., Kangur P. & Lek S., 2007. Patterning long-term changes of fish community in large shallow Lake Peipsi. *Ecol Modell* 203 (1-2): 34–44.

Keith P., Persat H., Feunteun E. & Allardi J., 2011. *Les Poissons d'eau douce de France*.

Kobler A., Klefoth T., Wolter C., Fredrich F. & Arlinghaus R., 2008. Contrasting pike (*Esox lucius* L.) movement and habitat choice between summer and winter in a small lake. *Hydrobiologia* 601 (1): 17.

Liermann C.R., Nilsson C., Robertson J. & Ng R., 2012. Implications of dam obstruction for global freshwater fish diversity. *BioScience* 62: 539–548.

Logez M., Roy R., Tissot L. & Argillier C., 2016. Effects of water-level fluctuations on the environmental characteristics and fish-environment relationships in the littoral zone of a reservoir. *Fundam Appl Limnol* 189 (1): 37–49.

McAllister D., Craig J., Davidson N., Delany S. & Seddon M., 2001. *Biodiversity Impacts of Large Dams*. 64 pp.

Morales J.M. & Ellner S.P., 2002. Scaling up animal movements in heterogeneous landscapes: the importance of behavior. *Ecology* 83 (8): 2240–2247.

Nathan R., Getz W.M., Revilla E., Holyoak M., Kadmon R., Saltz D. & Smouse P.E., 2008. A movement ecology paradigm for unifying organismal movement research. *Proc Natl Acad Sci* 105 (49): 19052–19059.
Hirsch P.E., Eloranta A.P., Amundsen P.-A., Brabrand Å, Charmasson J., Helland I.P., Power M., Sánchez-Hernández J., Sandlund O.T., Sauterleute J.F., Skoglund S., Ugedal O. & Yang H., 2017. Effects of water level regulation in alpine hydropower reservoirs: an ecosystem perspective with a special emphasis on fish. *Hydrobiologia* 794 (1): 287–301.

Hjorth P. & Bengtsson L., 2012. Large dams, statistics and critical review. In: *Encyclopedia of Lakes and Reservoirs* (L. Bengtsson, R.W. Herschy, R.W. Fairbridge Eds.) pp. 475–479.

Horký P., Slavík O. & Bartoš L., 2008. A telemetry study on the diurnal distribution and activity of adult pikeperch, *Sander lucioperca* (L.), in a riverine environment. *Hydrobiologia* 614 (1): 151.

Hovel K.A. & Regan H.M., 2008. Using an individual-based model to examine the roles of habitat fragmentation and behavior on predator-prey relationships in seagrass landscapes. *Landsc Ecol* 23 (1): 75–89.

Hudon C., Armellin A., Gagnon P. & Patoine A., 2010. Variations in water temperatures and levels in the St. Lawrence River (Québec, Canada) and potential implications for three common fish species. *Hydrobiologia* 647 (1): 145–161.

Hudon C., Gagnon P., Amyot J.-P., Létourneau G., Jean M., Plante C., Rioux D. & Deschênes M., 2005. Historical changes in herbaceous wetland distribution induced by hydrological conditions in Lake Saint-Pierre (St. Lawrence River, Quebec, Canada). *Hydrobiologia* 539 (1): 205–224.

Huuskonen H., Piironen J., Syväranta J., Eronen R., Biasi C., Kiiskinen P., Kortet R. & Vainikka A., 2019. Diet and movements of pikeperch (*Sander lucioperca*) in a large oligotrophic lake with an exceptionally high pikeperch yield. *Ecol Freshw Fish* 28 (4): 533–543.

Imbrock F., Appenzeller A. & Eckmann R., 2005. Diel and seasonal distribution of perch in Lake Constance: a hydroacoustic study and *in situ* observations. *J Fish Biol* 49: 1–13.

Inskip P.D., 1982. Habitat suitability index models: northern pike. In: FWS/OBS.

Jacobsen L., Berg S., Baktoft H. & Skov C., 2015. Behavioural strategy of large perch *Perca fluviatilis* varies between a mesotrophic and a hypereutrophic lake. *J Fish Biol* 86 (3): 1016–1029.

Jeppesen E., Mehner T., Winfield I.J., Kangur K., Sarvala J., Gerdeaux D., Rask M., Malmquist H.J., Holmgren K. & Volta P., 2012. Impacts of climate warming on the long-term dynamics of key fish species in 24 European lakes. *Hydrobiologia* 694 (1): 1–39.

Kaczka L.J. & Miranda L.E., 2014. Size of age-0 crappies (*Pomoxis* spp.) relative to reservoir habitats and water levels. *J Freshw Ecol* 29 (4): 525–534.

Kangur K., Park Y.-S., Kangur A., Kangur P. & Lek S., 2007. Patterning long-term changes of fish community in large shallow Lake Peipsi. *Ecol Modell* 203 (1-2): 34–44.

Keith P., Persat H., Feunteun E. & Allardi J., 2011. *Les Poissons d'eau douce de France*.

Kobler A., Klefoth T., Wolter C., Fredrich F. & Arlinghaus R., 2008. Contrasting pike (*Esox lucius* L.) movement and habitat choice between summer and winter in a small lake. *Hydrobiologia* 601 (1): 17.

Liermann C.R., Nilsson C., Robertson J. & Ng R., 2012. Implications of dam obstruction for global freshwater fish diversity. *BioScience* 62: 539–548.

Logez M., Roy R., Tissot L. & Argillier C., 2016. Effects of water-level fluctuations on the environmental characteristics and fish-environment relationships in the littoral zone of a reservoir. *Fundam Appl Limnol* 189 (1): 37–49.

McAllister D., Craig J., Davidson N., Delany S. & Seddon M., 2001. *Biodiversity Impacts of Large Dams*. 64 pp.

Morales J.M. & Ellner S.P., 2002. Scaling up animal movements in heterogeneous landscapes: the importance of behavior. *Ecology* 83 (8): 2240–2247.

Nathan R., Getz W.M., Revilla E., Holyoak M., Kadmon R., Saltz D. & Smouse P.E., 2008. A movement ecology paradigm for unifying organismal movement research. *Proc Natl Acad Sci* 105 (49): 19052–19059.
Overton J., Toner D., Policar T. & Kucharczyk D., 2015. Commercial production: factors for success and limitations in European percid fish culture. 881–890.

Patterson T.A., Thomas L., Wilcox C., Ovaskainen O. & Matthiopoulos J., 2008. State-space models of individual animal movement. *Trends Ecol Evol* 23 (2): 87–94.

Pekcan-Hekim Z., Horppila J., Nurminen L. & Niemistö J., 2005. Diel changes in habitat preference and diet of perch (*Perca fluviatilis*), roach (*Rutilus rutilus*) and white bream (*Abramis björkna*). *Archiv für Hydrobiologie Special Issues Advances in Limnology*, 59: 173–187.

Pierce C.L., Rasmussen J.B. & Leggett W.C., 1994. Littoral fish communities in southern quebec lakes: relationships with limnological and prey resource variables. *Can J Fish Aquat Sci* 51 (5): 1128–1138.

Pierce R.B., 2012. Northern pike: ecology, conservation, and management history. *Environ Hist* 18 (3): 650–651.

Plichard L., Forcellini M., Le Coarer Y., Capra H., Carrel G., Enochard R. & Lamouroux N., 2020. Predictive models of fish microhabitat selection in multiple sites accounting for abundance overdispersion. *River Res Appl* 36 (7):1056-1075

Poulet N., Lek S. & Argillier C., 2005. Pikeperch habitat use within a canal network in spring. *J Fish Biol* 67 (5): 1460–1474.

Prchalová M., Kubečka J., Cech M.F., Draštík J. V., Hohausova E.M., Peterka J.J.R., Tušer M. & Vasek M., 2009. The effect of depth, distance from dam and habitat on spatial distribution of fish in an artificial reservoir. *Ecol Freshw Fish* 18: 247–260.

Prchalová M., Kubečka J., Vašek M., Peterka J., Sedá J., Júza T., Ríha M., Jarolím O., Tušer M., Kratochvíl M., Čech M., Draštík V., Frouzová J. & Hohausová E., 2008. Distribution patterns of fishes in a canyon-shaped reservoir. *J Fish Biol* 73 (1): 54–78.

Radinger J., Matern S., Klefoth T., Wolter C., Feldhege F., Monk C.T. & Arlinghaus R., 2023. Ecosystem-based management outperforms species-focused stocking for enhancing fish populations. *Science* 379 (6635): 946–951.

Railback S.F., Lamberson R.H., Harvey B.C. & Duffy W.E., 1999. Movement rules for individual-based models of stream fish. *Ecol Modell* 123 (2): 73–89.

Rehage J.S., Blanchard J.R., Boucek R.E., Lorenz J.J. & Robinson M., 2016. Knocking back invasions: variable resistance and resilience to multiple cold spells in native vs. nonnative fishes. *Ecosphere* 7 (6): e01268.

Ríha M., Rabaneda-Bueno R., Jarić I., Souza A.T., Vejřík L., Draštík V., Blabolil P., Holubová M., Júza T., Gjelland K.Ø., Rychtecký P., Sajdlová Z., Kočvara L., Tušer M., Prchalová M., Sedá J. & Peterka J., 2022. Seasonal habitat use of three predatory fishes in a freshwater ecosystem. *Hydrobiologia* 849 (15): 3351–3371.

Rosenfeld J.S., 2017. Developing flow-ecology relationships: implications of nonlinear biological responses for water management. *Freshw Biol* 62 (8): 1305–1324.

Roy R., 2014. Distribution spatiale et activité des poissons en milieu lacustre : impacts des facteurs environnementaux à partir d'une approche multi-échelle. Application à la retenue des Bariousses. Thèse de doctorat, Aix Marseille université. 224 pp.

Roy R., Beguin J., Argillier C., Tissot L., Smith F., Smedbol S. & De Oliveira E., 2014. Testing the VEMCO Positioning System: spatial distribution of the probability of location and the positioning error in a reservoir. *Anim Biotelemetry* 2 (1): 1–7.

Roy R., Tissot L. & Argillier C., 2021. Environmental drivers of fish spatial distribution and activity in a reservoir with water level fluctuations. *Hydroécol Appl* 21: 25–46.

Schmieder K., 2004. European lake shores in danger – concepts for a sustainable development. *Limnologica* 34: 3–14.

Schulze T., Baade U., Dörner H., Eckmann R., Haertel-Borer S.S., Hölker F. & Mehner T., 2006. Response of the residential piscivorous fish community to introduction of a new predator type in a mesotrophic lake. *Can J Fish Aquat Sci* 63 (10): 2202–2212.

Skov C. & Koed A., 2004. Habitat use of 0+ year pike in experimental ponds in relation to cannibalism, zooplankton, water transparency and habitat complexity. *J Fish Biol* 64: 448–459.
Overton J., Toner D., Policar T. & Kucharczyk D., 2015. Commercial production: factors for success and limitations in European percid fish culture. 881–890.

Patterson T.A., Thomas L., Wilcox C., Ovaskainen O. & Matthiopoulos J., 2008. State-space models of individual animal movement. *Trends Ecol Evol* 23 (2): 87–94.

Pekcan-Hekim Z., Horppila J., Nurminen L. & Niemistö J., 2005. Diel changes in habitat preference and diet of perch (*Perca fluviatilis*), roach (*Rutilus rutilus*) and white bream (*Abramis björkna*). *Archiv für Hydrobiologie Special Issues Advances in Limnology*, 59: 173–187.

Pierce C.L., Rasmussen J.B. & Leggett W.C., 1994. Littoral fish communities in southern quebec lakes: relationships with limnological and prey resource variables. *Can J Fish Aquat Sci* 51 (5): 1128–1138.

Pierce R.B., 2012. Northern pike: ecology, conservation, and management history. *Environ Hist* 18 (3): 650–651.

Plichard L., Forcellini M., Le Coarer Y., Capra H., Carrel G., Enochard R. & Lamouroux N., 2020. Predictive models of fish microhabitat selection in multiple sites accounting for abundance overdispersion. *River Res Appl* 36 (7):1056-1075

Poulet N., Lek S. & Argillier C., 2005. Pikeperch habitat use within a canal network in spring. *J Fish Biol* 67 (5): 1460–1474.

Prchalová M., Kubečka J., Cech M.F., Draštík J. V., Hohausova E.M., Peterka J.J.R., Tušer M. & Vasek M., 2009. The effect of depth, distance from dam and habitat on spatial distribution of fish in an artificial reservoir. *Ecol Freshw Fish* 18: 247–260.

Prchalová M., Kubečka J., Vašek M., Peterka J., Sedá J., Júza T., Ríha M., Jarolím O., Tušer M., Kratochvíl M., Čech M., Draštík V., Frouzová J. & Hohausová E., 2008. Distribution patterns of fishes in a canyon-shaped reservoir. *J Fish Biol* 73 (1): 54–78.

Radinger J., Matern S., Klefoth T., Wolter C., Feldhege F., Monk C.T. & Arlinghaus R., 2023. Ecosystem-based management outperforms species-focused stocking for enhancing fish populations. *Science* 379 (6635): 946–951.

Railback S.F., Lamberson R.H., Harvey B.C. & Duffy W.E., 1999. Movement rules for individual-based models of stream fish. *Ecol Modell* 123 (2): 73–89.

Rehage J.S., Blanchard J.R., Boucek R.E., Lorenz J.J. & Robinson M., 2016. Knocking back invasions: variable resistance and resilience to multiple cold spells in native vs. nonnative fishes. *Ecosphere* 7 (6): e01268.

Ríha M., Rabaneda-Bueno R., Jarić I., Souza A.T., Vejřík L., Draštík V., Blabolil P., Holubová M., Júza T., Gjelland K.Ø., Rychtecký P., Sajdlová Z., Kočvara L., Tušer M., Prchalová M., Sedá J. & Peterka J., 2022. Seasonal habitat use of three predatory fishes in a freshwater ecosystem. *Hydrobiologia* 849 (15): 3351–3371.

Rosenfeld J.S., 2017. Developing flow-ecology relationships: implications of nonlinear biological responses for water management. *Freshw Biol* 62 (8): 1305–1324.

Roy R., 2014. Distribution spatiale et activité des poissons en milieu lacustre : impacts des facteurs environnementaux à partir d'une approche multi-échelle. Application à la retenue des Bariousses. Thèse de doctorat, Aix Marseille université. 224 pp.

Roy R., Beguin J., Argillier C., Tissot L., Smith F., Smedbol S. & De Oliveira E., 2014. Testing the VEMCO Positioning System: spatial distribution of the probability of location and the positioning error in a reservoir. *Anim Biotelemetry* 2 (1): 1–7.

Roy R., Tissot L. & Argillier C., 2021. Environmental drivers of fish spatial distribution and activity in a reservoir with water level fluctuations. *Hydroécol Appl* 21: 25–46.

Schmieder K., 2004. European lake shores in danger – concepts for a sustainable development. *Limnologica* 34: 3–14.

Schulze T., Baade U., Dörner H., Eckmann R., Haertel-Borer S.S., Hölker F. & Mehner T., 2006. Response of the residential piscivorous fish community to introduction of a new predator type in a mesotrophic lake. *Can J Fish Aquat Sci* 63 (10): 2202–2212.

Skov C. & Koed A., 2004. Habitat use of 0+ year pike in experimental ponds in relation to cannibalism, zooplankton, water transparency and habitat complexity. *J Fish Biol* 64: 448–459.
Skov C. & Nilsson A., 2018. *Biology and Ecology of Pike*. CRC Press, 410 pp.

Strayer D.L. & Findlay S.E.G., 2010. Ecology of Freshwater Shore Zones. *Aquat Sci* 72 (2): 127–163.

Świerzowski A., Godlewska M. & Półtorak T., 2000. The relationship between the spatial distribution of fish, zooplankton and other environmental parameters in the Solina reservoir, *Poland. Aquat Living Resour* 13 (5): 373–377.

Thorpe J.E., 1977. Morphology, physiology, behavior and ecology of *Perca fluviatilis* and *P. flavescens* Mitchell. *J Fish Board Can* 34: 1504–1514.

Van Winkle W., Jager Y. & Holcomb B., 1998. An individual-based model for sympatric populations of brown and rainbow trout for instream flow assessment: model description and calibration. *Ecol Modell* 10: 175–207.

Vanderbosch D. & Galatowitsch S., 2010. An Assessment of urban lakeshore restorations in Minnesota. *Ecol Restor* 28: 71–80.

Vašek M., Kubečka J., Matěna J. & Seďa J., 2006. Distribution and diet of 0+ Fish within a Canyon-Shaped European Reservoir in Late Summer. *Int Rev Hydrobiol* 91 (2): 178–194.

Vašek M., Prchalová M., Ríha M., Blabolil P., Čech M., Draštík V., Frouzová J., Jůza T., Kratochvíl M., Muška M., Peterka J., Sajdlová Z., Šmejkal M., Tušer M., Vejřík L., Znachor P., Mrkvička T., Seďa J. & Kubečka J., 2016. Fish community response to the longitudinal environmental gradient in Czech deep-valley reservoirs: implications for ecological monitoring and management. *Ecol Indic* 63: 219–230.

Vehanen T. & Lahti M., 2003. Movements and habitat use by pikeperch (Stizostedion lucio-perca (L.)) in a hydropeaking reservoir. *Ecol Freshw Fish* 12 (3): 203–215.

Westrelin S., Cucherousset J., Roy R., Tissot L., Santoul F. & Argillier C., 2021. Habitat partitioning among three predatory fish in a temperate reservoir. *Ecol Freshw Fish* 31 (1): 129–142.

Westrelin S., Roy R., Tissot-Rey L., Bergès L. & Argillier C., 2018. Habitat use and preference of adult perch (Perca fluviatilis L.) in a deep reservoir: variations with seasons, water levels and individuals. *Hydrobiologia* 809 (1): 121–139.

Wetzel R.G., 1990. Reservoir ecosystems: conclusions and speculations. In: *Reservoir limnology: ecological perspective* (K.W. Thornton, B.L. Kimmel, F.E. Payne Eds.) 11 pp.

Wu H., Chen J., Xu J., Zeng G., Sang L., Liu Q., Yin Z., Dai J., Yin D., Liang J. & Ye S., 2019. Effects of dam construction on biodiversity: a review. *J Clean Prod* 221: 480–489.

Zohary T. & Gasith A., 2014. The littoral zone. In: *Lake Kinneret: Ecology and Management* (T. Zohary, A. Sukenik, T. Berman, A. Nishri Eds.) pp. 517–532.

Zohary T. & Ostrovsky I., 2011. Ecological impacts of excessive water level fluctuations in stratified freshwater lakes. *Inland Waters* 1: 47–59.

**Cite this article as:** Paul Miguet, Laurence Tissot, Patrick Lambert, Ine S. Pauwels, Samuel Westrelin, An individual-based model to predict the spatial distribution of Northern pike, European perch and Pikeperch from habitat use in a reservoir subject to water level fluctuations. **24**, 1 (2024)
Skov C. & Nilsson A., 2018. *Biology and Ecology of Pike*. CRC Press, 410 pp.

Strayer D.L. & Findlay S.E.G., 2010. Ecology of Freshwater Shore Zones. *Aquat Sci* 72 (2): 127–163.

Świerzowski A., Godlewska M. & Półtorak T., 2000. The relationship between the spatial distribution of fish, zooplankton and other environmental parameters in the Solina reservoir, *Poland. Aquat Living Resour* 13 (5): 373–377.

Thorpe J.E., 1977. Morphology, physiology, behavior and ecology of *Perca fluviatilis* and *P. flavescens* Mitchell. *J Fish Board Can* 34: 1504–1514.

Van Winkle W., Jager Y. & Holcomb B., 1998. An individual-based model for sympatric populations of brown and rainbow trout for instream flow assessment: model description and calibration. *Ecol Modell* 10: 175–207.

Vanderbosch D. & Galatowitsch S., 2010. An Assessment of urban lakeshore restorations in Minnesota. *Ecol Restor* 28: 71–80.

Vašek M., Kubečka J., Matěna J. & Seďa J., 2006. Distribution and diet of 0+ Fish within a Canyon-Shaped European Reservoir in Late Summer. *Int Rev Hydrobiol* 91 (2): 178–194.

Vašek M., Prchalová M., Ríha M., Blabolil P., Čech M., Draštík V., Frouzová J., Jůza T., Kratochvíl M., Muška M., Peterka J., Sajdlová Z., Šmejkal M., Tušer M., Vejřík L., Znachor P., Mrkvička T., Seďa J. & Kubečka J., 2016. Fish community response to the longitudinal environmental gradient in Czech deep-valley reservoirs: implications for ecological monitoring and management. *Ecol Indic* 63: 219–230.

Vehanen T. & Lahti M., 2003. Movements and habitat use by pikeperch (Stizostedion lucio-perca (L.)) in a hydropeaking reservoir. *Ecol Freshw Fish* 12 (3): 203–215.

Westrelin S., Cucherousset J., Roy R., Tissot L., Santoul F. & Argillier C., 2021. Habitat partitioning among three predatory fish in a temperate reservoir. *Ecol Freshw Fish* 31 (1): 129–142.

Westrelin S., Roy R., Tissot-Rey L., Bergès L. & Argillier C., 2018. Habitat use and preference of adult perch (Perca fluviatilis L.) in a deep reservoir: variations with seasons, water levels and individuals. *Hydrobiologia* 809 (1): 121–139.

Wetzel R.G., 1990. Reservoir ecosystems: conclusions and speculations. In: *Reservoir limnology: ecological perspective* (K.W. Thornton, B.L. Kimmel, F.E. Payne Eds.) 11 pp.

Wu H., Chen J., Xu J., Zeng G., Sang L., Liu Q., Yin Z., Dai J., Yin D., Liang J. & Ye S., 2019. Effects of dam construction on biodiversity: a review. *J Clean Prod* 221: 480–489.

Zohary T. & Gasith A., 2014. The littoral zone. In: *Lake Kinneret: Ecology and Management* (T. Zohary, A. Sukenik, T. Berman, A. Nishri Eds.) pp. 517–532.

Zohary T. & Ostrovsky I., 2011. Ecological impacts of excessive water level fluctuations in stratified freshwater lakes. *Inland Waters* 1: 47–59.

**Cite this article as:** Paul Miguet, Laurence Tissot, Patrick Lambert, Ine S. Pauwels, Samuel Westrelin, An individual-based model to predict the spatial distribution of Northern pike, European perch and Pikeperch from habitat use in a reservoir subject to water level fluctuations. **24**, 1 (2024)