Spawning and Habitat Management in Salmonid Populations at the Southern Edge of Their Natural Ranges

Universidad Politécnica de Madrid Escuela Técnica Superior de Ingenieros de Montes

Spawning and habitat management in salmonid populations at the southern edge of their natural ranges

Reproducción y gestión del hábitat en poblaciones de salmónidos en el extremo meridional de sus distribuciones naturales

PhD Dissertation / Tesis Doctoral Javier María Gortázar Rubial Ingeniero de Montes 2015

PROGRAMA DE DOCTORADO EN INVESTIGACIÓN FORESTAL AVANZADA

ESCUELA TÉCNICA SUPERIOR DE INGENIEROS DE MONTES

SPAWNING AND HABITAT MANAGEMENT IN SALMONID

POPULATIONS AT THE SOUTHERN EDGE OF THEIR

NATURAL RANGES

[REPRODUCCIÓN Y GESTIÓN DEL HÁBITAT EN

POBLACIONES DE SALMÓNIDOS EN EL EXTREMO

MERIDIONAL DE SUS DISTRIBUCIONES NATURALES]

JAVIER MARÍA GORTÁZAR RUBIAL

Ingeniero de Montes

DIRECTOR

DIEGO GARCÍA DE JALÓN LASTRA

Doctor Ingeniero de Montes

Madrid, 2015

Tribunal nombrado por el Mgfco. y Excmo. Sr. Rector de la Universidad

Politécnica de Madrid, el día ...... de ...... de 2015

Presidente D......

Vocal D......

Secretario D......

Realizado del acto de defensa y lectura de la Tesis el día ......

de ...... de 2015 en Madrid.

Calificación …………………………………………………

EL PRESIDENTE LOS VOCALES

EL SECRETARIO

A mis padres A mis hermanos …y a Jacobo, que acaba de llegar

AGRADECIMIENTOS Fábula en dos escenas

Escena 1 – Sólo no puedes, con amigos sí.

(Otoño. Mediodía de un día muy soleado. Un pequeño prado junto a un río. Al lado de la orilla, MAX y JAVIER conversan.)

MAX: Bueno ¿qué? ¿ya has terminado la tesis? JAVIER: Casi. Sólo me falta escribir los agradecimientos. MAX: Eso es lo más fácil ¿no? JAVIER: Es que me gustaría hacer algo original, pero no se me ocurre el qué. MAX: ¿Y a quién vas a agradecer? JAVIER: Uff… a un montón de gente. Y seguro que se me olvida alguno… MAX: Bueno, lo típico es empezar por tu Director de Tesis. JAVIER: ¡Claro! A Diego García de Jalón, pero no por dirigirme la tesis… MAX: ¿Ein? JAVIER: Bueno sí, por eso también. Pero le agradezco más otras cosas que me ha enseñado… MAX: ¿Sobre ríos y truchas? JAVIER: ¿Eh…? Claro, eso también. Pero ahora me refiero a otras cosas… por ejemplo… ¡a buscarme la vida! Con Diego he hecho un máster en buscarme la vida, y se lo agradezco. También he aprendido de él… no sé, un cierto equilibrio mental, emocional… espiritual si quieres, sostenido a lo largo de los años, que… (Pausa.) Bueno ¡vale!, reconozco que esto no me lo he aprendido muy bien. Me pregunto cómo lo hace: Se ocupa de las cosas sin preocuparse, parece que nada le quita el sueño… Le seguiré observando, a ver si descubro su secreto. MAX: Vaya ¿Y a quién más? JAVIER: Pues a mi compañero Carlos Alonso, ése el primero sin duda. MAX: Claro, es que habéis hecho un buen equipo los dos. JAVIER: ¿Eh…? Sí, claro. Hemos hecho cosas chulas… Pero ahora eso es lo de menos. Carlos es un tipo fantástico. Si le pides ayuda te la da. Y si él intuye que la necesitas, aunque no se la pidas también está ahí. Podría decir muchas cosas buenas de él, pero destacaré sólo una: la capacidad que tiene para estar únicamente en lo que está en cada momento. ¿Y sabes una cosa? Al principio esto ¡me parecía un problema! ¿Cómo te quedas? Bueno, yo también he cambiado en estos años… menos mal. MAX: Oye, ¿y qué hay de la empresa? ¿no vas a decir nada? JAVIER: ¡Claro, Ecohidráulica! La montamos hace ya diez años y a mí me ha permitido trabajar en lo que quería… y también me ha impedido terminar antes esta tesis, jeje. Pero lo que importa de la empresa son las personas y ahí están, además de Carlos, Pilar Vizcaíno, siempre dispuesta a echarte una mano y con una sonrisa por delante, Miguel Marchamalo con su entusiasmo perenne y contagioso, Domingo Baeza, toda una fuerza de la naturaleza…

MAX: Vale ¿Y quién más? JAVIER: ¡Pues los compañeros que he tenido en el Laboratorio! Siempre ha habido un ambiente fantástico allí, ¡incluso cuando no se hacían seminarios! Pero son tantos… Me acuerdo que cuando empecé estaban Ezequiel, Julio, Ana y Javi Herranz. Ahora, muchos años después, ha vuelto Javi y están también Vanesa, Judit, Gonzalo, Chema… además de Marta González del Tánago, Joaquín Solana, Fernando Torrent. También he aprendido mucho trabajando con o para gente como Fernando Alonso, Mónica Espinosa, Llanos Gabaldón, Gonzalo Alonso, Paco Martínez Capel, Joe Rogers, Paulino Martínez, José María Montoro, los Agentes del Parque Natural Sierra de Castril… y ¿sabes? Javier Lobón me ayudó mucho cuando estaba empezando y andaba bastante perdido… me dio un buen empujón. MAX: Y ya está ¿no? JAVIER: Y debo agradecer también a los supervisores que tuve en mis estancias: Piotr Parasiewicz en USA y Patrick Davaine y Eddy Beall en Francia, estos dos últimos ya se han jubilado, creo. ¡Ah! Gracias también a Vincenzo Caputo y Hervé Capra que revisaron la tesis para obtener la Mención Internacional. Y no me olvido de las personas que durante estos años han trabajado conmigo en la empresa y también de aquéllos que vinieron a ayudarme en los trabajos de campo de la tesis: mi hermano Bux, Patricia, Carmen, Dani Sisí, David Hernández, David Nuevo, David el de Torrejón, Hugo, Hélène, Matthias, Virginia… MAX: Y fin. JAVIER: Y gracias también a mis amigos… MAX: ¿¡Pero qué dices!? ¿Tus amigos te han ayudado a hacer la tesis? JAVIER: Pues en general no, pero me han dado cosas mucho mejores y más importantes. MAX: Vamos, que más que agradecer a quien te haya ayudado en tu tesis, vas a hacer un repaso a tu vida reciente ¿no? JAVIER: Pues ahora que lo dices sí, un poco sí… Es que me acuerdo mucho de los años en que empezaba la tesis, cuando vivía con Cristina, Kubiak, con María y Nacho que ahora me cuidan tanto… Y echábamos cervezas y jugábamos al tenis con Protón, las diosas, los del Isabel y con quien pasaba por allí después del trabajo… (Se queda pensando, como ensoñado.) …y gracias a Pablo y José por estar siempre cerca a su manera… aunque yo haya estado lejos a la mía. MAX: Oye, que tendrás que ir terminando, digo yo… JAVIER: Y recuerdo los años que pasé en La Laguna con Olga… y con la pandilla de allí, Alejadro que me terminó de enganchar a la bici, Yeray, Pili, Álvaro, Gema, Álvaro, ¡Lola, cómo te quiero!… y a Víctor también, ¿eh? (Pausa. Sigue ensoñado.) Jo, la verdad es que hemos pasado ratos tan buenos… ¡Coño! Y a Diego Abánades, gracias por escribirme los agradecimientos… o ayudarme. MAX: ¿Y quién coño es Diego Abánades? JAVIER: Pues Diego es un… (Se lo piensa) medio-dramaturgo… que tenía un quiosco y que… Bueno, la de Diego es otra historia. MAX: Mejor. JAVIER: Y finalmente a mis hermanos y sobre todo a mis padres. Yo creo que la tesis ésta les va a hacer más ilusión a ellos que a mí mismo. MAX: Pues ya está. (Javier se zambulle en el río de un salto.)

OSCURO

Escena 2 – Despedida

(Atardecer del mismo día. El mismo pequeño prado junto al mismo río. Max está nadando en una poza, mientras Javier descansa sentado en la hierba, junto a la orilla. Al fondo, el cielo se está nublando, se prepara una tormenta.)

MAX: Vamos a ver si yo me he enterado. Cuando acabes esto, ¿quién se va a leer tu tesis? JAVIER: Prácticamente nadie. MAX: Y para algo que no se va a leer nadie, tú te has tirado catorce años, ¿correcto? JAVIER: Correcto. MAX: ¿Hace falta que saque conclusiones o ya te lo dices tú solito? JAVIER: No es tan fácil. Se suponía que no iba a llevar tanto tiempo… Al principio, digo. MAX: Ya. Espera que me ría. (Silencio.) ¿El resultado es relevante, al menos? ¿Vamos a vivir mejor después de esta tesis? Las truchas, digo. JAVIER: No. MAX: ¿Y los humanos? ¿Le vais a sacar vosotros partido, de algún modo? JAVIER: No sé. No creo. MAX: Ya. ¿Y tengo que sacar yo conclusiones, repito, o ya te las sacas tú solito? (Silencio. No hay más que decir.) JAVIER: (Se levanta, mira a Max con cierta tristeza.) Adiós. (Se da la vuelta y se va alejando despacio camino del pueblo. Por el río llega nadando EVA.) EVA: ¿Se ha ido ya Javier? MAX: Ahora mismo. EVA: ¿Estuviste muy duro? MAX: Alguien tenía que decírselo, ¿no? EVA: Le voy a echar de menos, creo. MAX: También yo. Catorce años son muchos años, ¿verdad? (Contemplan en silencio el horizonte por donde se perdió Javier.) Bueno, ¿cerramos ya la tesis? EVA: ¡Espera! Antes tengo que decirte una cosa. MAX: (Que se lo ve venir) Ni se te ocurra. EVA: Vamos, es una tesis sobre truchas, no podemos cerrar sin decirlo… MAX: Está bien. Pero acaba rápido… (Max cierra los ojos y aprieta los dientes como si le fuera a doler. Eva se regodea, disfruta el momento, sonríe de branquia a branquia antes de decir…) EVA: Te quiero mucho… ¡¡como la trucha al trucho!! (Sonoro beso.) ¿Lo ves? No fue para tanto. Ahora sí que podemos cerrar. MAX: (Mira a Eva con amor.) Bonita, ¿vamos subiendo donde las gravas? EVA: ¡Sí! Que ya estamos casi en diciembre. (Max y Eva comienzan a nadar juntos hacia aguas arriba. Junto a la poza sólo quedan algunas huellas de las botas de Javier, pero la inminente lluvia las borrará pronto.)

OSCURO

INDEX / ÍNDICE

1. ABSTRACT / RESUMEN ...... 1 1.1. Abstract ...... 3 1.2. Resumen ...... 4 2. GENERAL INTRODUCTION AND OBJECTIVES ...... 7 2.1. General Introduction ...... 9 Populations in the southern limit of the species’ range ...... 11 Populations’ trends ...... 13 Mediterranean climate and climate change ...... 14 Habitat management ...... 15 2.2. Objectives ...... 16 3. SPAWNING PERIOD OF A SOUTHERN BROWN TROUT POPULATION ...... 19 3.1. Introduction...... 21 3.2. Material and methods ...... 22 3.3. Results ...... 27 3.4. Discussion ...... 33 4. REDD SUPERIMPOSITION IN RELATION TO SPAWNING HABITAT AVAILABILITY ...... 37 4.1. Introduction...... 39 4.2. Material and methods ...... 39 4.3. Results ...... 43 4.4. Discussion ...... 46 5. MICROHABITAT SIMULATION OF INSTREAM HABITAT ENHANCEMENT OPTIONS ...... 51 5.1. Introduction...... 53 5.2. Materials and methods ...... 55 5.3. Results ...... 59 5.4. Discussion ...... 68 6. MESOHABSIM APPLICATION FOR RIVER RESTORATION ...... 71 6.1. Introduction...... 73 6.2. Methods ...... 73 6.3. Results ...... 78 6.4. Discussion ...... 81 xiii

7. GENERAL DISCUSSION ...... 87 7.1. General Discussion ...... 89 Brown trout spawning ...... 89 Redd superimposition ...... 92 Physical habitat management ...... 95 Habitat assessment case studies ...... 97 8. CONCLUSIONS / CONCLUSIONES ...... 103 8.1. Conclusions...... 105 8.2. Conclusiones ...... 107 9. HYPOTHESES FOR FURTHER RESEARCH / HIPÓTESIS PARA LA INVESTIGACIÓN FUTURA . 109 9.1. Hypotheses for further research ...... 111 9.2. Hipótesis para la investigación futura ...... 111 10. REFERENCES ...... 113

xiv

1. ABSTRACT / RESUMEN

Abstract / Resumen

1.1. Abstract

Salmonid populations in the Iberian Peninsula (brown trout, Salmo trutta; and Atlantic salmon, Salmo salar) are close to the southern limit of their natural ranges, and therefore they are of great importance for the conservation of the species. In the present dissertation, some aspects of spawning and habitat management have been investigated, in order to improve the knowledge on these southern salmonid populations.

Brown trout spawning have been studied in the river Castril (Andalusia, southern Spain), and it has been observed that spawning occurs from December until April with the maximum activity in February. This finding represents one of the most belated and protracted spawning periods within the natural range of the species. Furthermore, it is now known that the rest of Andalusian populations show similar (belated and extended) spawning periods.

Broad-scale analyses throughout the brown trout natural range showed that latitude partly explained both spawning mean time (R2 = 62.8%) and spawning duration (R2 = 24.4%) by negative relationships: the lower the latitude, the later the spawning time and the longer the spawning period. It is plausible that a long spawning period would be an advantage for survival of trout populations in unpredictable habitats, and thus the following hypothesis has been proposed, which is yet to be tested: spawning duration is longer in unpredictable than in predictable habitats.

High rate of redd superimposition observed in the river Castril was not only caused by high density of spawners. Trout females chose specific sites for redd construction instead of randomly dispersing over the suitable spawning habitat. These observations suggest that female spawners have some kind of preference for superimposing redds. Moreover, in limestone streams such as Castril, unused gravels can be very cohesive and hard to dig, and thus redd superimposition may be an advantage for female, because digging may require less energy expenditure in already used redd sites than in cohesive and embedded unused sites. Hence, the following hypothesis has been proposed, which is yet to be tested: females have a higher preference for superimposing redds in streambeds with cohesive and embedded substrates than in rivers with loose gravels.

Within the topic of habitat management, two different approaches have been used for physical habitat assessment, in order to quantify the potential change in habitat availability, prior to the actual implementation of proposed habitat measures.

Firstly, physical habitat for Atlantic salmon in the river Pas (Cantabria, northern Spain) has been assessed at the microhabitat scale, using the IFIM approach along with a two dimensional hydraulic model (River2D). Proposed habitat enhancement actions have been simulated and potential habitat change has been quantified. Results showed a very small increasing in habitat availability and therefore it is not worth to implement these measures in this stream reach.

Secondly, physical habitat for brown trout in the river Tajuña (Guadalajara, central Spain) has been assessed at the mesohabitat scale, using the MesoHABSIM approach.

Abstract / Resumen

The river Tajuña is currently impacted by surrounding agricultural uses, and thus restoration was designed to mitigate these impacts and to drive the river to a more natural state. Habitat availability after the planned restoration has been quantified, and the results have permitted to identify in which sites the restoration will be more effective.

1.2. Resumen

Las poblaciones de salmónidos en la Península Ibérica (trucha común, Salmo trutta; y salmón atlántico, Salmo salar) se encuentran cerca del límite meridional de sus distribuciones naturales, y por tanto tienen una gran importancia para la conservación de estas especies. En la presente Tesis se han investigado algunos aspectos de la reproducción y de la gestión del hábitat, con el objeto de mejorar el conocimiento acerca de estas poblaciones meridionales de salmónidos.

Se ha estudiado la reproducción de la trucha común en el río Castril (Andalucía, sur de España), donde se ha observado que la freza ocurre desde diciembre hasta abril con el máximo de actividad en febrero. Este hecho representa uno de los periodos reproductivos más tardíos y con mayor duración de toda la distribución natural de la especie. Además, actualmente se sabe que el resto de poblaciones andaluzas tienen periodos de reproducción similares (retrasados y extendidos).

Análisis en la escala de la distribución natural de la trucha común, han mostrado que la latitud explica parcialmente tanto la fecha media de reproducción (R2 = 62.8%) como la duración del periodo de freza (R2 = 24.4%) mediante relaciones negativas: a menor latitud, la freza ocurre más tarde y durante más tiempo. Es verosímil que un periodo de freza largo suponga una ventaja para la supervivencia de las poblaciones de trucha en hábitats impredecibles, y por tanto se ha propuesto la siguiente hipótesis, que deberá ser comprobada en el futuro: la duración de la freza es mayor en hábitats impredecibles que en aquellos más predecibles.

La elevada tasa de solapamiento de frezaderos observada en el río Castril no se explica únicamente por una excesiva densidad de reproductores. Las hembras de trucha eligieron lugares específicos para construir sus frezaderos en vez de dispersarse aleatoriamente dentro del hábitat adecuado para la freza que tenían disponible. Estas observaciones sugieren que las hembras tienen algún tipo de preferencia por solapar sus frezaderos. Además, en ríos calizos como el Castril, las gravas pueden ser muy cohesivas y difíciles de excavar, por lo que el solapamiento de frezaderos puede suponer una ventaja para la hembra, porque la excavación en sustratos que han sido previamente removidos por frezas anteriores requerirá menos gasto de energía que en sustratos con gravas cohesivas que no han sido alteradas. Por tanto, se ha propuesto la siguiente hipótesis, que deberá ser comprobada en el futuro: las hembras tienen una mayor preferencia por solapar sus frezaderos en ríos con sustratos cohesivos que en ríos con sustratos de gravas sueltas.

Abstract / Resumen

En el marco de la gestión del hábitat, se han empleado dos enfoques diferentes para la evaluación del hábitat físico, con el objeto de cuantificar los cambios potenciales en la disponibilidad de hábitat, antes de la implementación real de determinadas medidas sobre el hábitat.

En primer lugar, se ha evaluado el hábitat físico del salmón atlántico en el río Pas (Cantabria, norte de España), en la escala del microhábitat, empleando la metodología IFIM junto con un modelo hidráulico bidimensional (River2D). Se han simulado una serie de acciones de mejora del hábitat y se han cuantificado los cambios en el hábitat bajo estas acciones. Los resultados mostraron un aumento muy pequeño en la disponibilidad de hábitat, por lo que no sería efectivo implementar estas acciones en este tramo fluvial.

En segundo lugar, se ha evaluado el hábitat físico de la trucha común en el río Tajuña (Guadalajara, centro de España), en la escala del mesohábitat, empleando la metodología MesoHABSIM. Actualmente, el río Tajuña está alterado por los usos agrícolas de sus riberas, y por tanto se ha diseñado una restauración para mitigar estos impactos y para llevar al río a un estado más natural. Se ha cuantificado la disponibilidad de hábitat tras la restauración planteada, y los resultados han permitido identificar los tramos en los que la restauración resultaría más eficaz.

2. GENERAL INTRODUCTION AND OBJECTIVES

General Introduction and Objectives

2.1. General Introduction

Salmonids (class Actinopterygii, order Salmoniformes, family Salmonidae) are native to the Northern Hemisphere and include freshwater and anadromous species (Nelson 2006). They are medium to large size fishes inhabiting almost all habitats with clear, cold and well oxygenated water (Kottelat & Freyhof 2007). In the Iberian Peninsula there are two native salmonid species, both of them included in the genus Salmo: Atlantic salmon (Salmo salar Linnaeus, 1758) and brown trout (Salmo trutta Linnaeus, 1758).

Atlantic salmon is native to both east and west coasts of the North Atlantic Ocean (Klemetsen et al. 2003). Within Europe, it ranges from the Kara drainages (Kara Sea, western Siberia), Scandinavia and Iceland in the north, through the Atlantic European basins, to the river Miño (Spain and Portugal) in the south (Kottelat & Freyhof 2007; Nielsen et al. 2013). In turn, brown trout is native to Europe, northern Africa and western Asia (MacCrimmon et al. 1970). The northernmost trout populations are in Iceland, northern Scandinavia and Russia (Elliott 1994), and the southern limit is in the Atlas Mountains of north-west Africa (Snoj et al. 2011). Therefore, the Iberian populations of both species are in the southern limit of their natural distribution.

Moreover, both species have been introduced into several regions outside their native ranges: Atlantic salmon is nowadays present in New Zealand, Chile and southern Argentina (Kottelat & Freyhof 2007). Brown trout is considered one of the world’s most invasive fish (Budy et al. 2013), and it has been introduced into eastern Russia, Pakistan, India, Nepal, Japan, New Zealand, Australia, southern and montane eastern Africa, USA, Canada and South America (Klemetsen et al. 2003; Kottelat & Freyhof 2007).

Within the Iberian Peninsula, Atlantic salmon inhabits several basins throughout the Cantabrian coast and Galicia, from the Bidasoa basin in the east to the Miño river in the west (Fig. 2.1, Doadrio 2001). There are also historical records from Duero, Tagus and Guadiana rivers, but they are now extirpated, hence nowadays salmon populations are restricted to the north of the river Miño (Doadrio 2001; Kottelat & Freyhof 2007). For its part, brown trout inhabits headwaters in almost all Spanish rivers, except in some east and south Iberian streams and in the Guadiana basin (Fig. 2.1, Doadrio 2001). Both species can hybridize in the northern Iberian Peninsula (García de Leániz & Verspoor 1989; Beall et al. 1997; Castillo et al. 2008; Álvarez & García-Vázquez 2011) as well as in other geographical regions (Jansson & Öst 1997; Gephard et al. 2000; Matthews et al. 2000; Ayllón et al. 2004; Urke et al. 2013), and their hybrids can be fertile (García- Vázquez et al. 2003, 2004).

Atlantic salmon is anadromous, spending part of their life at sea, and returning to freshwater to spawn on gravel riverbeds with cold and oxygenated water (Doadrio 2001). In contrast, brown trout show a continuum of different life history strategies, with anadromous, lacustrine and resident populations (Milner et al. 2003; Cucherousset et al. 2005). In the Iberian Peninsula there are anadromous trout populations in the Cantabrian coast, Galicia and north of Portugal, while resident populations occur in the

General Introduction and Objectives rest of the rivers. The southern limit of anadromy is in the Lima river (Portugal, Antunes et al. 2006). Although there are important morphological, ecological and demographical differences between anadromous and resident trouts, they can live in sympatry and reproduce (Caballero et al. 2006), because they just perform different strategies belonging to the same species (life history continuum, Cucherousset et al. 2005).

Figure 2.1. Atlantic salmon (Salmo salar) and brown trout (Salmo trutta) distributions within Spain (Doadrio 2001), indicanting the study sites: rivers Castril (chapters 3 and 4), Pas (chapter 5) and Tajuña (chapter 6).

Currently, there is certain controversy on brown trout taxonomy. Trout shows such variability in phenotypic appearance and ecology (Pakkasmaa & Piironen 2001) that about 50 species have been described for varieties of brown trout (Behnke 1986). Later on, all of them have been grouped together in one species, according to the distribution described above. It was then considered that Salmo trutta is a unique polymorphic species (Elliott 1994) which shows a continuum of life history tactics (Milner et al. 2003; Cucherousset et al. 2005). Recently, Kottelat & Freyhof (2007) considered that the species Salmo trutta only corresponds to the anadromous Atlantic trout (including the Duero basin as the southern limit) and the resident and lacustrine populations directly derived from it, excluding some populations of the northern Pyrenean slope and Iberian Peninsula. According to these authors, the populations of the rest of the Iberian Peninsula apparently represent several distinct, unnamed and unstudied species. Since

General Introduction and Objectives

Kottelat & Freyhof (2007) emphasized that trout taxonomy need an important re- examination, I will consider in this dissertation that all the Iberian brown trout populations belong to the single species Salmo trutta.

In Spain, Atlantic salmon enters the rivers from October to August and the gonadal maturation concludes in the river (Doadrio 2001). They spawn from middle November to early February, while the maximum reproductive activity usually takes place in December (García de Leániz et al. 2003). Juvenile salmons grow in freshwater during two-three years before smolting and migrating to the sea. Usually after two-three years in the sea, adult salmons return to the river to spawn (Doadrio 2001). The Atlantic salmon is iteroparous (Klemetsen et al. 2003) and some individuals can reproduce up to four times in their life. Furthermore, some parrs (mainly males) can precociously mature before smolting and can successfully reproduce with sea migrant females (Dalley et al. 1983; Myers et al. 1986; Myers & Hutchings 1987).

Brown trout (also resident and anadromous individuals) spawn during autumn or winter in running waters, burying their eggs on gravel bottoms (Haury et al. 1999). Eggs hatch in the subsequent spring and the fry stay under the gravel feeding on their yolk sac (Klemetsen et al. 2003; Alonso González et al. 2010). The duration of egg incubation and endogenous larval feeding are dependent on water temperature, being the longer the lower the temperature (Crisp 1988; Elliott & Hurley 1998). When most of the yolk is absorbed, fry emerge from the gravel and start feeding in the water column near the spawning ground (Klemetsen et al. 2003; Alonso González et al. 2010). In resident populations, brown trout may remain close to their natal reach, but often they move about frequently (Crisp 1993; Gowan et al. 1994). If there is a lake (or a similar habitat, such as a reservoir) accessible to the population, many individuals may gradually move there for feeding (Jonsson 1989) because this habitat can provide higher growth (Frost & Brown 1967; Crisp et al. 1990), but others may become stream resident as well (Jonsson 1985). In anadromous populations, brown trout may live in coastal waters during summer, mainly in shallow waters (Lyse et al. 1998; Knutsen et al. 2001), and usually do not travel far offshore in the Atlantic. Anadromous trouts can stay at sea for two or more years before returning to the river for spawning (Went 1962; Jensen 1968; Jonsson & Jonsson 2002), or they can spend only one summer at sea, as it is frequent in the river Ulla (Galicia, Spain, Caballero Javierre 2002; Caballero et al. 2006). In Spain, brown trout usually attains sexual maturity at two or three years of age (Doadrio 2001).

Populations in the southern limit of the species’ range

As shown above, both Iberian salmon and trout populations are in the southern limit of their natural distribution. There is a vast scientific literature dealing with salmon and trout biology and ecology, but little of this work has focused on the southernmost natural populations. However, the studies on southern salmonids have grown considerably in recent times (e.g., Consuegra & García de Leániz 2006; Lobón-Cerviá 2007; Nicola et al. 2009; Alonso et al. 2011a; Horreo et al. 2011; Almodóvar et al. 2012; Ayllón et al. 2012; Parra et al. 2012; Larios-López et al. 2015a, 2015b). In order to make an appropriate management of these peripheral populations, it is important to improve the knowledge on their biology and ecology, because salmonids in low-latitude populations may face environmental constraints quite different from those of the main distribution area (Nicola et al. 2008).

General Introduction and Objectives

Peripheral populations tend to occupy less favourable habitats and to occur at lower and more variable densities than those from the centre of the geographical distribution (Lawton 1993; Channell & Lomolino 2000; Vucetich & Waite 2003). Likewise, the proportion of sites occupied by the species usually decreases from the centre to the margin of the distribution (Lawton 1993), and thus marginal populations are typically restricted to particular habitat islands within a matrix of unsuitable landscapes (Hampe & Petit 2005). Isolated and patchy populations may have reduced resilience to face stochastic events, particularly when carrying capacity is low (Morita & Yokota 2002). As a consequence, these marginal populations may be more prone to extinction than those from the centre of the species range (Lawton 1993; Vucetich & Waite 2003).

Population dynamics vary across the range of a species in response to environmental changes (Lawton 1993; Brown et al. 1996; Case & Taper 2000). Because of the lower densities in peripheral populations, the regulation of these populations could switch from density-dependent processes at the centre to density-independent ones at the margin (Guo et al. 2005; Antonovics et al. 2006). Moreover, density-independent factors may have a higher influence on demographic parameters and produce greater fluctuations in peripheral than centrally situated populations (Mehlman 1997; Williams et al. 2003; Giralt & Valera 2006; Thingstad et al. 2006). Haldane (1956) posed the hypothesis that population regulation should be driven by endogenous (density- dependent) factors in favourable habitats and by exogenous (density-independent) factors in unfavourable ones. According to this, it is likely that the marginal Iberian populations were regulated by environmental factors, rather than density-dependence. Indeed, several studies revealed that hydrology is a major factor regulating population size in Iberian brown trout (Lobón-Cerviá 2003, 2004, 2007, 2009; Lobón-Cerviá & Rincón 2004; Alonso-González et al. 2008; Nicola et al. 2009; Lobón-Cerviá et al. 2011), and a sound relationship between the North Atlantic Oscillation (NAO, a broad climatic index) and population growth has also been found in Navarra (northern Iberian Peninsula, Alonso et al. 2011a). Furthermore, a recent large-scale analysis of data from many Spanish brown trout populations (Alonso & Gortázar 2014) did not find evidence of negative density-dependent regulation, and it also showed that cohort size is determined by recruitment, which in turn is largely controlled by environmental factors (mainly flow and also temperature). These latest results extend earlier conclusions from a Cantabrian population (e.g., Lobón-Cerviá 2003, 2007; Lobón-Cerviá & Rincón 2004) to the Iberian Peninsula.

The small size and prolonged isolation of peripheral populations have produced low within-population genetic diversity (Castric & Bernatchez 2003; Petit et al. 2003; Chang et al. 2004), but also high genetic differentiation among such populations, even between nearby ones (Comps et al. 2001; Castric & Bernatchez 2003; Hampe et al. 2003; Petit et al. 2003; Martin & McKay 2004). This low intra-population and high inter-population genetic diversity has been observed in Iberian brown trout (Machordom et al. 2000; Bouza et al. 2001; Suárez et al. 2001; Sanz Ball-llosera et al. 2002; Antunes et al. 2006; Sanz et al. 2006; Almodóvar et al. 2007; Egmasa 2009). In Atlantic salmon, the usually strong homing behaviour can result in reproductive isolation, genetic differentiation (even among nearby rivers, Fontaine et al. 1997; Spidle et al. 2001; Verspoor et al. 2002), likely local adaptation (Taylor 1991; García de Leániz et al. 2007; Verspoor et al. 2007) and genetic drift, especially in small populations (Storfer 1999; Moran 2002). However, overlapping generations and the

General Introduction and Objectives presence of mature male parr (Jones & Hutchings 2001; Taggart et al. 2001) can increase the effective population size, particularly in southern latitudes (Utrilla & Lobón-Cerviá 1999; Martínez et al. 2000; García-Vázquez et al. 2001; Juanes et al. 2007). Furthermore, some gene flow occurs by straying to neighbouring populations (Fraser et al. 2007; Dionne et al. 2008) and this dispersal may increase in peripheral populations (García de Leániz et al. 2007). For instance, Iberian salmon populations maintain relatively high levels of genetic diversity despite their small size and declining status, and they seem to avoid genetic drift by means of dispersal and gene flow (Consuegra et al. 2005; Consuegra & García de Leániz 2006; Kuparinen et al. 2009).

Local adaptation and genetic drift are dominant processes in the dynamics of marginal populations (Hampe & Petit 2005), and these populations may often be relatively better- adapted to unfavorable conditions but perform poorly under most other conditions (Hoffmann & Blows 1994). In these peripheral populations, selection is expected to act for local adaptation rather than for generalism (Dynesius & Jansson 2000), and this (along with reduced gene flow) can lead to the development of distinct ecotypes (Hampe & Bairlein 2000; Castric & Bernatchez 2003; Pérez-Tris et al. 2004; Snoj et al. 2011) and even to the start of speciation processes (Martin & McKay 2004). Moreover, most of these relict populations have not been the source of major postglacial recolonizations, thereby preserving a high genetic distinctiveness (Bilton et al. 1998; Petit et al. 2003), and thus they commonly harbour the bulk of the species genetic diversity (Lesica & Allendorf 1995; Petit et al. 2003; Hewitt 2004).

Therefore, the populations inhabiting the southern margins of the species distribution ranges, are very important for the long-term conservation of genetic diversity, phylogenetic history and evolutionary potential of the species, and their research and conservation deserve high priority (Hampe & Petit 2005).

Populations’ trends

During the last decades, wild stocks of Atlantic salmon have declined throughout their native range, including local extinctions in several river systems mainly in low latitudes (Parrish et al. 1998; WWF 2001; Klemetsen et al. 2003; ICES 2013). In the Iberian Peninsula, salmon populations have also suffered this decline (Braña et al. 1995; Doadrio 2001; García de Leániz et al. 2001; Morán et al. 2009; Álvarez et al. 2010). The causes of this dramatic decline have been attributed to overexploitation, interaction with farmed salmon (which can transmit diseases and cause genetic introgression) and habitat alteration, which includes water pollution, physical habitat degradation (channelization, gravel extraction), flow manipulation and habitat fragmentation by the presence of barriers to migration (e.g., Doadrio 2001; WWF 2001; Álvarez et al. 2010).

Abundance trends in brown trout are not as clear as in Atlantic salmon. However, declines have been observed in some areas of the native range, such as Switzerland (Burkhardt-Holm & Scheurer 2007; Burkhardt-Holm 2008) or Wales (Clews et al. 2010), as well as in the Iberian Peninsula (Alonso & Gortázar 2014; Alonso et al. 2014) and within some of its territories: e.g., Navarra (GAN S.A. 2014), Andalusia (Egmasa 2009; Sáez Gómez 2010; Larios-López et al. 2015a), although there are some cases of range enlargement by artificial propagation (Aparicio et al. 2000). Overexploitation by recreational angling (Braña et al. 1992; Almodóvar & Nicola 1998, 2004), genetic

General Introduction and Objectives introgression caused by restocking with non-native trout (García-Marín & Pla 1996; Cagigas et al. 1999; Machordom et al. 1999, 2000; Almodóvar et al. 2001, 2006; Leunda 2010) hybridization with Atlantic salmon (Castillo et al. 2008), predation by allochthonous fish, water pollution, habitat degradation and flow alteration by the presence of hydraulic facilities (García de Jalón et al. 1988, 1992; Almodóvar & Nicola 1999; Liebig et al. 1999; Alonso-González et al. 2008), have been cited among the major threats to Iberian brown trout populations (Almodóvar 2001; Doadrio 2001).

Mediterranean climate and climate change

Many of the Iberian brown trout populations are subjected to a Mediterranean climate, characterised by seasonal events of flooding and drying and by strong interannual flow variations (Gasith & Resh 1999; Tierno de Figueroa et al. 2013). Summer drought occurs every year in these Mediterranean streams, and it is likely to be a critical factor for survival of young trout (Elliott et al. 1997; Nicola et al. 2009) and thus for the strength and viability of the population.

Some climate change predictions for the Mediterranean region forecast pronounced decrease in precipitation and pronounced warming, along with increased interannual temperature variability, and all these effects would be more marked in summer (Giorgi & Lionello 2008). It should be noted than climate change effects on freshwater ecosystems will affect not only temperature but also key drivers such as precipitation and flow regime (Wenger et al. 2011). According to very recent estimates on climate change, mean temperature and total precipitation in the Iberian Peninsula will change throughout the present century. Specifically, mean temperatures will increase and total precipitation is likely to decrease, and these changes will be more marked in summer (Pérez & Boscolo 2010; IPCC 2013). As explained above, Iberian salmonids have to face recurrent summer droughts, along with higher temperatures and stronger flow variations than in central Europe where they encounter more stable and appropriate conditions.

Therefore, the non-optimal conditions that salmonids have to face in southern habitats are expected to worsen in future decades because of climate change, and some estimates have been drawn about the implications of these changes: In the watersheds inhabited by brown trout, populations are likely to retreat to higher altitudes, because of increased water temperatures (Ficke et al. 2007; Matulla et al. 2007; Filipe et al. 2013; Santiago et al. 2014). Likewise, it has been predicted a northward movement of anadromous salmonids, along with population extinctions in the southern edge of the ranges (Buisson et al. 2008; Jonsson & Jonsson 2009; Lassalle & Rochard 2009). The risk of local extirpation will be greater for populations living in fragmented habitats or in areas with east-west oriented mountain systems, such as the Iberian Peninsula (Ficke et al. 2007). Furthermore, global warming is likely to expose salmonids to increased threats from parasites and contagious fish diseases (Ficke et al. 2007; Jonsson & Jonsson 2009) and to produce shifts in several life-history traits (Jonsson & Jonsson 2009).

Global warming has already produced shifts in the distributions and abundances of many thermally sensitive species (Peñuelas et al. 2002; Root et al. 2003; Thomas et al. 2004; Parmesan 2006; Nielsen et al. 2013), and evidences suggest that warming have already reduced some European salmonid populations (Winfield et al. 2010; Jeppesen et

General Introduction and Objectives al. 2012), including brown trout (Hari et al. 2006) and Atlantic salmon (Todd et al. 2008). Likewise, the observed decline of several Iberian trout populations has been explained by a loss of suitable thermal habitat during the last decades, as a result of regional warming (Almodóvar et al. 2012). In the southern Iberian region of Andalusia indeed, brown trout has experienced a range contraction and a displacement towards higher altitudes since the XIX century, along with the extinction of the western populations where altitudes are lower than in the eastern mountain systems (Sáez Gómez 2010; Larios-López et al. 2015a).

Habitat management

The key importance of stream habitat for aquatic communities is widely recognized, hence in Europe the EU Water Framework Directive1 has significantly reinforced river protection and restoration in order to preserve and improve the ecological status of water bodies (González del Tánago et al. 2012). Focusing on salmonids, they are strongly influenced by their habitat, which defines the distributions and abundances of populations (Armstrong et al. 2003). Habitat can be viewed as being composed of three components, which are indissolubly inter-related (Hendry et al. 2003): water quality, water quantity (the hydrological regime) and physical habitat (the physical structure of the river environment).

Part of the present dissertation focuses on the management of salmonid physical habitat, which is determined by the interaction of the structural features of the channel and the hydrological regime (Maddock 1999). When the structural features of the river channel (e.g., gradient, channel size and shape, bank structure, substrate size) are combined with a particular discharge level, a distinct pattern of hydraulic features (e.g., depths, velocities, hydromorphologic units) is produced. It is a combination of these two attributes that make up the physical habitat, and therefore it is dynamic both in space and in time (Maddock 1999).

Physical habitat provides the natural link between the physical environment and their inhabitants, and hence it is a very useful element for evaluating river health and its conservation status (Harper et al. 1992; Maddock 1999). The management of physical habitat is a valuable tool to rehabilitate and improve salmonid populations, and its benefits can be very positive when compared to other measures such as stocking (Hendry et al. 2003). However, in order to succeed in developing management schemes and measures, it is also necessary to address the wider catchment problems affecting the fish communities (Kauffman et al. 1997; Hendry et al. 2003; Cowx & van Zyll de Jong 2004), and particularly to understand the critical factors leading to deterioration, i.e. the “bottlenecks”, which can be overcome by specific habitat measures (García de Jalón 1995; Rosenfeld & Hatfield 2006).

Stream habitats are subjected to natural disturbance events (e.g., spates) which may vary in frequency, magnitude, duration and predictability (Waples et al. 2009). These natural disturbances produce changes in stream habitats, but nowadays the largest alterations are by far caused by human intervention (Hendry et al. 2003; Collares-Pereira & Cowx 2004; Garófano-Gómez et al. 2013). Habitat restoration schemes are designed to reduce

1 Directive 2000/60/EC of the European Parliament and of the Council, of 23 October 2000, establishing a framework for Community action in the field of water policy.

General Introduction and Objectives the impacts of human activities and to bring the river ecosystem closer to its natural structure and function. In order to attain an almost natural condition, physical habitat restoration has been successfully developed in a wide range of river types impaired by different pressures (e.g., O’Grady 1993, 1995; House 1996; Kelly & Bracken 1998; Woolsey et al. 2007; Antón et al. 2011; González del Tánago et al. 2012).

Furthermore, the benefits of physical habitat restoration may extend far beyond the improvement of salmonid populations (even if this was the main objective), because restoration must be designed to achieve a natural physical form of the channel and banks. If this is properly designed and developed, then the natural ecology of the river ecosystem would start to recover, bringing benefits to a wide diversity of flora and fauna (Hendry et al. 2003).

2.2. Objectives

As it has been outlined above, southern populations are very important for the long-term conservation of the species, and they currently face serious risks, such as the ongoing climate change (which shall be more pronounced in Mediterranean regions) and the multiple stressors they are subjected to. To preserve these southern populations, management actions must be founded on a sound understanding of their particular biological and ecological characteristics. In the present dissertation I try to contribute to this necessary knowledge, by studying some aspects of spawning and habitat management in southern salmonid populations. Therefore, the overall aim of this Thesis is to improve the knowledge about southern salmonid populations, in order to make an appropriate management for their conservation.

When I started the work of this Thesis, not much was known about the distinctive features of brown trout populations in the region of Andalusia, which is located very close to the southernmost edge of the species range. This lack of knowledge is illustrated by e.g., the fact that some researchers (Kottelat & Freyhof 2007) have even suggested that southern Iberian trouts may not belong to Salmo trutta species.

In any case, available electrofishing data from the river Castril population (Andalusia) showed some surprising and interesting features. For instance, length-frequency histograms revealed two modes in the 0+ age-class distribution. Besides, very small trout (33 mm fork length) were caught as late as September and they still had traces of their recently re-absorbed yolk sac. These facts encouraged me to investigate on the life cycle of this population, and particularly on its reproduction timing.

This investigation is presented in the chapter 3, which describes the belated and protracted spawning period observed in the Castril population, and compares it with other natural populations across the species range. This chapter also discusses the hypothesis that an extended spawning is an advantage for survival in unpredictable habitats.

In the course of this investigation I also addressed the phenomenon of redd superimposition in this river, and this work led on to the chapter 4, which discusses its causes, arguing that habitat availability is not the only factor responsible for

General Introduction and Objectives superimposition and that trout females may have a preference for spawning over previously excavated redds.

Given the key importance and the usefulness of physical habitat management for the conservation of salmonid populations, chapters 5 and 6 focus on the evaluation and restoration of physical habitat. Two different techniques are used to simulate the outcome of different habitat management measures and thus to evaluate their effectiveness. In chapter 5, the Instream Flown Incremental Methodology (IFIM) is used, by means of a two dimensional hydraulic model, to evaluate particular habitat enhancement options in the river Pas (Cantabria, Spain) inhabited by Atlantic salmon. In chapter 6, the MesoHABSIM approach is used to evaluate the effectiveness (focusing on the brown trout population) of specific restoration measures in the river Tajuña (Guadalajara, Spain), which is altered by river rectification, channel entrenchment and incision, and degradation of riparian vegetation, along with flow depletion and regulation.

In order to reach the main goal stated above (to improve knowledge about southern salmonid populations and to support successful management for conservation), this Thesis pursues the following specific objectives:

- To characterise the spawning period of a southern brown trout population, by describing its timing and duration.

- To investigate the potential relation of spawning timing and duration to environmental conditions, particularly the flow regime unpredictability.

- To study the variation of these traits (mean spawning date and spawning duration) along latitudinal and altitudinal gradients within the natural distribution of the species, in order to elucidate large scale patterns in these traits.

- To describe the phenomenon of redd superimposition in a southern brown trout population.

- To investigate the causes of redd superimposition in this population, by determining (at the microhabitat scale) whether spawning habitat availability can explain redd superimposition or whether other factors are responsible.

- To evaluate (by means of habitat simulation at the microhabitat scale, using the Instream Flow Incremental Methodology and a two dimensional hydraulic model) whether the application of certain habitat enhancement measures will effectively improve the useable habitat for Atlantic salmon in a southern population, and to quantify this improvement.

- To evaluate (by means of the MesoHABSIM approach, a physical habitat model based on the identification of habitat attributes at the mesohabitat scale) whether the application of certain habitat restoration measures will effectively increase the available habitat for a southern brown trout population living in a river altered by agricultural uses.

General Introduction and Objectives

Therefore, in this Thesis I address some aspects of the biology and management of particular trout and salmon populations which live in the southern limit of the natural distributions of the species (the Iberian Peninsula). The Thesis is composed of four articles published in peer-reviewed scientific journals. Chapter 3 and chapter 4 were published in Ecology of Freshwater Fish, chapter 5 was published in Aquatic Ecology and chapter 6 in the journal Limnetica.

3. SPAWNING PERIOD OF A SOUTHERN BROWN TROUT POPULATION

This chapter has been published in a peer-reviewed international scientific journal:

Gortázar, J., García de Jalón, D., Alonso-González, C., Vizcaíno, P., Baeza, D. & Marchamalo, M. (2007). Spawning period of a southern brown trout population in a highly unpredictable stream. Ecology of Freshwater Fish, 16, 515–527.

Spawning period of a southern brown trout population

3.1. Introduction

The natural distribution area of brown trout (Salmo trutta L.) ranges within 71–33º north in Europe. The northernmost populations are in Iceland, northern Scandinavia and Russia, and the southern limit is in the mountain streams of northern Morocco (Elliott 1994). Close to this southern boundary, the region of Andalusia (southern Iberian Peninsula) maintains indigenous brown trout populations that have been isolated in mountain streams since the last glacial period. Despite the abundance of scientific literature on brown trout ecology and biology, very few studies have focused on the southernmost natural populations. Even within the Iberian Peninsula, most papers have dealt with northern or central populations.

Moreover, brown trout in Andalusia is endangered by human pressures, and has been declared “at risk of extinction” (Franco Ruiz & Rodríguez de los Santos 2001), so anglers are forbidden to kill their captures since 2005. This protection status highlights the importance of good management practices for the species’ conservation in this region. To fulfil this task, it is necessary to achieve a sound knowledge of the ecology and biology of brown trout in this latitude.

Geographical latitude has been shown to be related to several life history traits, like growth, age at first maturity, longevity, lifespan, proportion of repeat spawners and smolt age (Jonsson & L’Abée-Lund 1993). The most important factor responsible for these clines is probably water temperature (Elliott 1982). In particular, latitude affects the spawning period: brown trout spawn earlier the higher the latitude because of the lower water temperature and the longer egg incubation period (Klemetsen et al. 2003). For instance, the influence of low latitude on the spawning period has been documented in the river Aller (northern Spain, latitude: 43º10’N), where García & Braña (1988) reported that spawning took place from late January to late March. Such belated reproduction has important implications in fisheries management, as fishing should be forbidden during spawning and incubation in order to protect the recruitment. Populations located even further south are likely to show even later spawning dates.

The present study deals with the population in the river Castril, which is one of the best preserved in Andalusia and has no introgression of foreign trout alleles. It also shows very low genetic diversity due to a strong process of genetic drift, caused by the small population size and its isolation (P. Martínez, personal communication). The objectives of this study were to identify the spawning time and duration, the potential relation of these traits to environmental conditions, particularly the flow regime unpredictability, and to compare our findings on the spawning period with published data from other natural populations.

Redd counts were chosen to identify the spawning period, as they are less expensive and less disruptive than other population monitoring methods (Dunham et al. 2001). Salmonid redd counts have been used for a variety of purposes: to detect trends in the population size (Rieman & Myers 1997), to predict parr abundance (Beland 1996), to analyse the spatial variability of reproduction (Beard & Carline 1991; Mayo Rustarazo et al. 1995; Baxter et al. 1999) or to identify the spawning period of brown trout

Spawning period of a southern brown trout population populations (Champigneulle et al. 1988, 2003), among others. Redd counts are a relative index of the intensity of spawning, if several surveys are done during the whole spawning season (Champigneulle et al. 1988). We also chose this method for conservation reasons, since it does not require the sacrifice of any fish (Dunham et al. 2001).

3.2. Material and methods

Study site

The river Castril is an aquifer-fed mountain stream located in the province of Granada (Andalusia, southern Spain, Fig. 3.1), between 37º48’–37º54’ North and 2º46’ West. It rises at 1283 m a.s.l., in the limestone southern slopes of the Segura Mountains, and flows southward into the river Guadiana Menor, which is a tributary of the river Guadalquivir. The aquifer provides a rather constant water temperature throughout the year (yearly range in the headwaters: 8.8–10.2 ºC). This region has the Mediterranean climate, which makes the flow regime highly unpredictable: high flows usually occur in winter and/or spring, but there are also very dry years, and others with two or three peak flows which may occur in summer or autumn (Fig. 3.2).

Our study was conducted in the uppermost part of the river, where brown trout is the only fish species present. In the study area there are several impassable anthropogenic obstacles (Fig. 3.1), described here from upstream to downstream: a small hydroelectric dam in the headwaters; a 500 m long reach, where the river becomes subterranean, due to flow detraction upstream; the dam of El Portillo reservoir, which releases water from the bottom; and when passing through the town of Castril there are two ramps to dissipate hydraulic energy. Thus, the Castril brown trout population is divided into several isolated groups. The area upstream of the reservoir belongs to the Sierra de Castril Natural Park.

Spawning period of a southern brown trout population

France T Spain

Morocco

El Portillo reservoir

Figure 3.1. Map of the Iberian Peninsula showing the geographical situation of the river Castril. The location of the impassable obstacles (transverse lines), the town of Castril (light grey), redd counts reaches, electrofishing sites (open circles) and temperature data loggers (T) are also shown.

Spawning period of a southern brown trout population

16 1934-35 16 1935-36 16 1938-39 12 12 12 8 8 8 4 4 4 0 0 0 16 1939-40 1940-41 16 1941-42 16 12 12 12 8 8 8 4 4 4 0 0 0 16 1942-43 16 1943-44 16 1944-45 12 12 12 8 8 8 4 4 4 0 0 0 16 1945-46 16 1948-49 16 1949-50 12 12 12 /s)

3 8 8 8 4 4 4 0 0 0 20 1951-52 16 1950-51 16 12 12 8 8

Monthly mean flow (m flow mean Monthly 4 4 24 1954-55 0 0 20 16 1952-53 16 1953-54 16 12 12 12 8 8 8 4 4 4 0 0 0 16 1955-56 16 1966-67 16 1976-77 12 12 12 8 8 8 4 4 4 0 0 0 16 1978-79 16 1983-84 16 1988-89 12 12 12

8 8 8 4 4 4

0 0 0 Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Jun Aug Figure 3.2. Historical flow regimes for 23 years, illustrating interannual unpredictability.

Spawning period of a southern brown trout population

Redd counts

Water clarity, stream flow, gradient and width (ca. 8 m) enabled good visibility and working conditions during fieldwork. Redds were identified by their characteristic configuration, with a pit upstream followed immediately by a dome downstream, and by a lack of periphyton over the substrate, caused by female digging activities (Crisp & Carling 1989; Grost et al. 1991; Delacoste et al. 1993). Excavations without an obvious tailspill were not considered as redds. The relationships between redd tail length, horizontal dimensions of redds and fish length given by Crisp & Carling (1989) were used to estimate a minimum redd size. Thus, any redd shorter than 60 cm in total length (TL) was rejected in the analyses. For conservation reasons, we did not excavate any redd to check for the presence of eggs, but several were positively confirmed as redds by observing spawning trout.

A preliminary survey was conducted in the 2003–2004 season to roughly identify the spawning period and to select the reaches that would be surveyed the following year. Study reaches were chosen based on the presence of suitable spawning habitat and on their accessibility. Four visits to the stream were conducted during the preliminary survey: in late October, early December, mid-February and early March. These visits were conducted by walking along the riverbank without entering the riverbed, so the sampling effort was much lower than that of the following year (see below).

The main survey in 2004–2005 was conducted in four stream reaches (reaches 1–4, Fig. 3.1, Table 3.1). We decided to survey quite long stream reaches weekly, instead of intensively surveying small index areas, for the following reasons: (i) the expectation of a long spawning period; (ii) redd counts in limited segments can lead to a failure in detecting the areas where actual spawning is occurring, due to changes in the spatial distribution of reproduction (Dunham et al. 2001) and (iii) due to logistical constraints.

Table 3.1. Length, slope and altitude of the study reaches. Water Redd Length Slope Altitude Number Superimposed Reach temperature Density (m) (%) (m a.s.l.) of redds redds (ºC) (n/100m2) 1 811.5 2.59 1214 9.5±0.21 (8.8–9.9) 23 6 (26.1) 0.238 2 1577.6 2.85 1046 9.1±1.20 (6.6–11.6) 127 37 (29.1) 1.197 3 2538.8 1.77 956 8.8±2.33 (4.2–13.8) 295 155 (52.5) 1.440 4 706.6 0.57 850 8.2±0.85 (7.0–11.2) 18 2 (11.1) 0.167 Water temperature over the spawning season (20 December to 11 May): mean ± SD, range in brackets. Main results: number of redds, number of superimposed redds (percentage in brackets) and redd density.

All the redd surveys were performed by the same researcher, walking along the riverside in the areas where it was possible to see the riverbed well. In the rest of the areas he walked in the riverbed, taking care not to disturb the bottom substrate. Frequent visual verifications were made to ensure that the substrate was not altered by the surveyor. Furthermore, in the surveys conducted after the end of the spawning season no excavations were observed that could be confused with an actual redd, despite the fact that surveys were still being conducted.

Spawning period of a southern brown trout population

On each visit to the stream, reaches 1–4 were visually surveyed, walking from downstream to upstream to ensure good visibility. Every time a new redd was detected, its horizontal dimensions were measured (length and width of pit, dome and entire redd), a sketch was drawn and photographs were taken. The redd position was marked on an aerial photograph, and its coordinates were recorded with a Magellan SporTrak GPS. We also noted down the type of macrohabitat in which the redd was located, as defined by Simonson et al. (1993). The macrohabitat types considered were riffle, run, glide and pool, named from faster and shallower to slower and deeper. When the redd was located in the transition between two different macrohabitats, both of them were included. Redds were considered to be “close to the water’s edge” if they were <25 cm from the margin.

Any redd already recorded in a previous survey was measured again to see if it had increased in size, and it was compared with the previous sketch to check for changes in its configuration (position and size of pit and dome). Comparison of redd sketches, photographs and horizontal dimensions served to ascertain whether the redd remained unchanged or had increased due to superimposition. If the redd had changed, new measurements, photographs and a sketch were made.

Visual surveys of redds were conducted every week, except at the end of the season, when surveys were conducted every second week. Several problems (mainly increases in stream flow and turbidity) prevented us from surveying all the reaches on every visit to the stream. In particular, a high flow around 15 March impeded fieldwork on reaches 2 and 3 for 3 weeks.

Spawning period

To identify the temporal development of brown trout reproduction in the Castril, the accumulated number of redds was plotted against time. Following Knapp & Vredenburg (1996) we counted the number of new redds plus the number of redds that had changed significantly since the previous count. In this way, we also considered superimposed redds, thus minimizing the bias of the estimate of the actual redd numbers. The relationship between redd size and time was analysed by simple regression.

To compare our results with those from other populations, we carried out a literature review on the brown trout spawning period throughout the natural distribution area of the species. The data obtained from the bibliography were average spawning date, duration of the spawning period, latitude and altitude. The literature usually reports the spawning period with low precision (e.g., 7 or 15 days). We used the date in the middle of the spawning period reported in each paper as the average spawning date. The data were analysed by means of simple and multiple regressions.

Water temperature and flow data

Water temperature was recorded every 2 h at four sites on the river (Fig. 3.1) using Vemco Minilog data loggers. Historical flow data were recorded where the river flows into the reservoir. Daily mean flow data for the study season were obtained from the reservoir inflow and outflow data. All flow data were provided by the Confederación Hidrográfica del Guadalquivir (Guadalquivir Water Authority).

Spawning period of a southern brown trout population

Spawner data

Electrofishing was performed in February 2004 at six sites on the river (Fig. 3.1). Fork length and live weight were measured and scales were taken. Maturity was tested by gently squeezing fish to determine ripeness. At the sites connected with the reservoir, scales from mature trout were examined to find zones with high growth, typical of the reservoir phase (Champigneulle et al. 1988). This allowed us to determine whether each mature trout came from the reservoir or from the stream. A Mann–Whitney test was performed to compare the lengths of the two types of spawners (reservoir and stream dwelling).

3.3. Results

Spawning period

In the first visual survey in 2004–2005, five redds were found on reach 3, on 22 December, so reproduction had started before. The spawning season ended before 27 April, when the last redd was detected, and after 12 April, when the previous survey was conducted. We found two different patterns in the development of the spawning activity in the Castril (Fig. 3.3). On one hand, on reaches 2 and 3 (on the middle part of the river, connected to each other and to the reservoir), the spawning period lasted more than 3 months. It started in December or early January and ended in April. Spawning took place about 2 weeks later on reach 2 than on reach 3. On reach 3, there was more spawning activity at the beginning of the reproductive period, so 50% of the total redds were already recorded by 29 January. On reach 2, 50% of the total redd count was not attained until 22 February (Fig. 3.3). On the other hand, on reaches 1 (headwaters, isolated from the rest of the river) and 4 (downstream of the reservoir, also isolated), spawning only lasted approximately a month and a half. It started in the second week of February and ended in late March.

10% 50% 90% Reach 1

10% 50% 90% Reach 2

10% 50% 90% ReachReach 3

10% 50% 90% Reach 4

29-Nov 19-Dec 8-Jan 28-Jan 17-Feb 9-Mar 29-Mar 18-Apr 8-May Figure 3.3. Summary of the spawning period on the river Castril, showing 10%, 50% and 90% of the total redd count. The lines cover the time between the first and last observations of redds. The crosses represent the last survey with no redds prior to spawning.

Spawning period of a southern brown trout population

16 4 (a) Reach 1 (a) Reach 4

12 3 /s) 3 8 2 Flow (m Flow

Temperature (ºC)Temperature 4 1

0 0

20 100 (b) (b) Reach 1 Reach 4

80 15

60 10

40 redds New Accumulated redds (%) 5 20

0 0 20-Dec 17-Jan 14-Feb 14-Mar 11-Apr 9-May 20-Dec 17-Jan 14-Feb 14-Mar 11-Apr 9-May

16 4 (a) Reach 2 (a) Reach 3

12 3 /s) 3 8 2 Flow (m Flow

Temperature (ºC) 4 1

0 0

100 100 (b) (b) Reach 2 Reach 3 80 80

60 60 New redds New 40 40 Accumulated redds (%) redds Accumulated

20 20

0 0 20-Dec 17-Jan 14-Feb 14-Mar 11-Apr 9-May 20-Dec 17-Jan 14-Feb 14-Mar 11-Apr 9-May

Figure 3.4. (a) Water temperature (solid line) and flow (dashed line). (b) Number of new redds (column) recorded in each survey and accumulated redds (line) throughout the spawning period. The arrows show slight drops in reproductive intensity. The isolated reaches (1 and 4) are shown above, and reaches 2 and 3 below.

Spawning period of a southern brown trout population

In the middle of the spawning season, a slight drop in the number of new redds was detected on reaches 2 and 3, resulting in two peaks of reproductive intensity: one in the first half of February and the second in late February to early March (Fig. 3.4). This decrease happened one week later on reach 2, following the temporal pattern described above (delayed spawning on reach 2). This slight drop was related neither to water temperature nor to stream flow, and it was more drastic on reach 2.

Results from the preliminary survey conducted in 2003–2004 were consistent with those from the more intensive 2004–2005 sampling. In the former season no redds were seen in or before early December, 67.9% of the redds were detected in mid-February and 32.1% in early March.

Influence of stream flow and water temperature

The stream flow was moderate (ca. 1 m3/s) during the 2004–2005 spawning season, except for a spate (ca. 4 m3/s) on 15 March which lasted 2 weeks. On reach 4, the flow regime is altered by the reservoir, so it is completely different from the natural regime. Here, reproduction began after a slight drop in the flow, but, at the same time, as on reach 1. During the high flow on 15 March, reproduction continued on reaches 1 and 4, despite the fact that surveys were impeded on the more affected reaches (2 and 3). When the water level subsided on reaches 2 and 3, few more new redds were observed there (Fig. 3.4).

Because the Castril is fed by an aquifer, the water temperature is extremely constant, especially on reach 1, close to the main spring. Throughout the spawning period, the greatest variability was recorded on reach 3. In the headwaters (reach 1), the water temperature never went outside the interval of 8.8–9.9 ºC. The temperature on reach 4 is influenced by the reservoir bottom outlet, so it is colder and more constant than expected (Table 3.1). On the reaches with less variable temperature regimes (1 and 4), spawning was concentrated in February and March and it lasted for about a month and a half.

Spawner and redd features

During the electrofishing survey, 14 mature trout were captured. Eleven of them were stream dwelling (fork length ranged within 16.6–22.1 cm) and three were spawners from the reservoir (48–59.8 cm). Mature brown trout from the reservoir were significantly longer than stream-dwelling fish (Mann–Whitney test P < 0.05).

Reach 3 had the highest number of redds and the highest redd density, while the isolated reaches (1 and 4) showed the lowest values (Table 3.1). There was no significant relationship between total redd length and date on the isolated reaches (P = 0.92, Fig. 3.5a). However, in the part of the stream that is connected with the reservoir, a significant negative relationship was found (P = 0.001), although this only accounted for 5.03% of the variability (Fig. 3.5b). The largest redd on the isolated reaches was 2.1 m in TL. After 10 March no redds longer than 2.1 m were found on reaches 2 and 3.

Spawning period of a southern brown trout population

3 (a) Reaches 1 and 4

0 29-Nov 19-Dec 8-Jan 28-Jan 17-Feb 9-Mar 29-Mar 18-Apr 8-May 6 (b) Reaches 2 and 3

Redd legth (m) legth Redd 4

0 29-Nov 19-Dec 8-Jan 28-Jan 17-Feb 9-Mar 29-Mar 18-Apr 8-May Figure 3.5. Evolution of total redd length throughout the spawning period: (a) on isolated reaches and (b) on reaches connected with the reservoir. The regression line for reaches 2 and 3 (thick line) and the length of the largest redd found on the isolated reaches (dashed line) are given (see text for details).

In the Castril, 15.6% of the redds were close to the water’s edge, while 84.4% were in other parts of the channel. The largest proportion of redds (50.9%) were located in glides, 27.6% were in the transition to a shallower and faster macrohabitat (like the classical pool tailout), 18.7% were in riffles or runs, and a small percentage (2.8%) were in the middle of pools.

On reach 3 there was a high degree of superimposed redds: 52.5% of the redds were built over pre-existing ones (Table 3.1). Nearly half of the redd sites (45.5%) in the river were used more than once by spawning females, ranging from 1 to 9 different spawns over the same redd site.

Literature review on brown trout spawning period

Multiple regression analysis between the mean spawning date and latitude and altitude, showed that the independent variable altitude was not statistically significant (P = 0.45), so it was removed from the model. Simple regression between latitude and the mean spawning date yielded a significant relationship (P < 0.0001) which accounted for 62.84% of the variability (Fig. 3.6a). Furthermore, altitude was not significant (P = 0.29) in the multiple regression between the spawning period duration and latitude and altitude, and the model was not significant (P = 0.11). However, simple regression

Spawning period of a southern brown trout population between latitude and spawning duration yielded a significant relationship (P < 0.006) which accounted for 24.4% of the variability (Fig. 3.6b).

70 (a)

30 1-sep 1-oct 31-oct 30-nov 30-dic 29-ene 28-feb Spawning mean date 70

Latitude (North) (b)

30 0 20 40 60 80 100 120 140 Spawning duration (days) Figure 3.6. Simple regression analysis between latitude and (a) mean spawning date, and (b) duration of spawning period. Solid circles represent data from this study. Open circles are data from the literature (see Table 3.2).

Spawning period of a southern brown trout population

Table 3.2. Variables obtained from the literature for the performance of regression analyses: latitude, altitude (metres above sea level), average spawning date and duration of spawning period of the populations reviewed. Location Latitude Altitude Date Duration Reference (north) (m a.s.l.) (days) Finland 64º 00’ 4 October 22 (Keränen et al. 1974) SE Norway 61º 15’ 336 30 September 30 (Olsen & Vøllestad 2003) Brumunda 60º 55’ 4 October 38 (Rustadbakken et al. 2004) (Norway) Vosso 60º 48’ 28 14 November 61 (Barlaup et al. 1994) (W Norway) Mid Norway 63º 00’ 4 October 22 (Jonsson & Jonsson 1999) S Norway 58º 00’ 4 October 22 (Jonsson & Jonsson 1999) Gotland 58º 00’ 6 December 67 (Rubin et al. 2004) (Sweden) High-Rhine 46º 30’ 15 January 62 (Zeh & Dönni 1994) Girnock Burn 56º 40’ 10 November 53 (Stuart 1957) (Scotland) Kirk Burn 56º 40’ 18 November 69 (Campbell 1977) (Scotland) Teesdale 54º 35’ 489 30 October 15 (Crisp et al. 1990) (N England) Dorset 51º 20’ 8 January 14 (Mann 1971) (S England) Hampshire Avon 51º 00’ 30 November 14 (Mann 1971) (S England) Black Brows 54º 20’ 69 20 November 34 (Elliott 1984) Beck (England) Wilfin Beck 54º 20’ 24 November 43 (Elliott 1984) (England) Ardennes 50º 50’ 30 November 92 (Huet 1961) (Belgium) Czechoslovakia 49º 45’ 6 November 61 (Libosvarsky 1967, 1974) Petite Sarine 47º 00’ 22 November 61 (Schlunke & Vonlanthen (Switzerland) 2002) Brittany 48º 00’ 23 December 77 (Baglinière & Maisse 2002) (France) Lake Leman 46º 30’ 436 11 December 69 (Champigneulle et al. 1988) (France) Chevenne 46º 20’ 1100 4 December 60 (Champigneulle et al. 2003) (France) Pyrenees 42º 55’ 855 23 December 45 (Delacoste et al. 1993) (France) SW France 43º 20’ 300 10 January 80 (E. Beall, personal communication) Asturias 43º 10’ 920 21 February 59 (García & Braña 1988) (N Spain) Asturias 43º 00’ 15 December 61 (Toledo et al. 1993) (N Spain) Galicia 42º 43’ 200 10 January 72 (Caballero Javierre 2002) (NW Spain) Soria 41º 40’ 1125 22 November 15 (Lobón-Cerviá et al. 1986) (N Spain) Guadalajara 40º 20’ 15 December 51 (Mayo Rustarazo et al. (central Spain) 1995) Firniz 37º 45’ 730 23 December 77 (Alp et al. 2003) (S Turkey)

Spawning period of a southern brown trout population

3.4. Discussion

Spawning period

It was not possible to precisely identify the start of spawning, as five redds were already found in the first survey on reach 3. However, the preliminary survey showed that there were no redds in early December. Therefore, assuming that the spawning period does not change significantly between years (Anderson 1983; Champigneulle et al. 1988; Scott 1990; Sorensen et al. 1995), we can state that spawning in the Castril began in December.

The river Castril showed the latest date for brown trout reproduction mentioned in the literature: from December until April. However, this belated spawning period agrees with the known latitudinal cline: spawning is earlier the higher the latitude (Klemetsen et al. 2003). Our literature review clearly showed the influence of latitude on the spawning date, but it did not show the influence of altitude, although both variables are known to be related (Illies & Botosaneanu 1963). The lack of significance of this variable might be due to the scarcity of altitude data obtained from the literature review: stream altitude is shown in only 12 populations of 29 reviewed (Table 3.2).

Regression between spawning duration and latitude showed that the lower the latitude the longer the spawning period (Fig. 3.6b). The causes of this latitudinal cline might be linked to temperature and habitat predictability, although the low coefficient of determination reflects the high variability among stream habitats even at similar latitudes. As water temperature is not such a strong environmental constraint in warmer streams, southern trout populations might have the opportunity to mature and spawn over a wider time range. We believe that habitat predictability also influences the duration of the spawning period, as is explained below.

Historical flow records show that the river Castril is not a predictable habitat (Fig. 3.2). The Mediterranean climate of this region shapes the flow regime, making its interannual variations highly unpredictable: peak flows may occur at any time during the spawning season. We support the hypothesis that a long spawning period is an advantage for survival in unpredictable habitats. It is known that a catastrophic event (like a spate or a drought) may weaken the year’s recruitment if it happens at the critical stages of the life cycle (e.g., Frost & Brown 1967; Anderson 1983; Spina 2001; Cattanéo et al. 2002) and thus, it might put the population at risk. Specifically, stream flow has been highlighted as a major factor affecting the recruitment of stream brown trout (Jensen & Johnsen 1999; Alonso-González et al. 2004; Lobón-Cerviá 2004; Lobón-Cerviá & Rincón 2004; Lobón-Cerviá & Mortensen 2005). In unpredictable habitats, where it is not possible to know when a catastrophic event will occur, it is a great advantage to spawn over a long period, and in this way it is more likely that at least a part of the recruitment will survive each year. (Champigneulle et al. 2003) already suggested that spatio-temporal dispersion of reproduction is likely to impede population extinction. Several researchers have provided evidence for this hypothesis in other fish groups (Bye 1984; Munro 1990; Mazzoni & Iglesias-Rios 2002).

We have suggested that habitat predictability may be linked to the latitudinal cline found in our literature review: the lower the latitude the longer the duration of

Spawning period of a southern brown trout population spawning. Despite the huge variability in habitat predictability throughout the brown trout’s natural distribution range, northern streams might be somewhat more predictable than southern ones. If this was the case, it would support the hypothesis that unpredictability in southern habitats may be a reason for the longer spawning periods detected in southern populations.

Although the advantage of a long spawning period is clear in unpredictable habitats, its origin may be adaptive or phenotypic. The phenotypic plasticity of brown trout is well known and documented (e.g., Elliott 1994) and thus may be responsible for this characteristic. However, a genetic origin of this phenomenon cannot be ruled out, because these southern populations have been isolated in mountain streams since the last glacial retreat (ca. 13,000 years ago), and processes of speciation may well have been taking place. Brown trout populations which have naturally persisted in isolated headwaters may have developed adaptations to particular environmental conditions (Northcote 1992; Elliott 1994).

Influence of stream flow and water temperature

Increasing water flow is known to force brown trout to abandon redd construction, although some individuals may still be able to spawn near the water’s edge (Pender & Kwak 2002). On reaches 2 and 3, it was not possible to determine whether spawning was impeded or not during the spate, because surveys were aborted at that time. Sediment deposition during decreasing flows may have covered redds built during that period. Thus, it is possible that redd numbers after the spate were underestimated.

Low water temperature can lead to a cessation in spawning activity. Libosvarsky (1974, 1976) found a break in reproduction when the temperature fell below 3 ºC. Beall & de Gaudemar (1999) observed that spawning stops below 3–4 ºC for Atlantic salmon (Salmo salar L.). However, in the river Castril the water temperature was not so extreme. The coldest temperature was 4.2 ºC (Table 3.1), attained on reach 3 only for 2 days and no break was observed. Furthermore, the water temperature in the Castril was always within the brown trout “growth zone”, as defined by Elliott (1994).

Redd features

Beard & Carline (1991) reported roughly similar redd densities (0.02–1.06 redds/100 m2) to those observed in the Castril (Table 3.1). The highest redd density in our study (reach 3) was slightly lower than that reported by Rubin et al. (2004) for sea trout (1.56 redds/100 m2). The highest densities reported by Champigneulle et al. (2003) in a highly fragmented mountain stream (6.3 redds/100 m2) greatly exceeded the Castril redd densities. This suggests that habitat fragmentation may lead to an increase in redd densities in the areas with suitable spawning habitat.

Most of the redds in the Castril were situated in places where intragravel flow was likely to occur, as stated by previous researchers (Crisp & Carling 1989; Baglinière & Maisse 2002, among others). Many redds (15.6%) were excavated close to the water’s edge, more than in the river Dulce in central Spain (4.2%,) (Mayo Rustarazo et al. 1995). As most of the redds were built at or near base flow, there was no risk of dewatering for egg pockets laid beside the margins. We observed a similar proportion of redds in glides and pool tailouts than in river Dulce (Mayo Rustarazo et al. 1995).

Spawning period of a southern brown trout population

However, we found fewer redds in the middle of pools (10% in the Dulce) and more in riffles or runs (10% in the Dulce).

The spatial dispersion of redd sites among different macrohabitats may be a good system to avoid complete loss of the offspring if a catastrophic event happens (Champigneulle et al. 2003). Redds located in different macrohabitats may have different probabilities of being destroyed, for instance, by a high flow which may scour the gravels and the eggs (Kondolf et al. 1991), or by a drought, which may leave the shallower redds stranded.

The percentage of superimposed redds was very high in some parts of the stream, especially on reach 3 (Table 3.1). Superimposition rates in the Castril were among the highest values reported in the literature for brown trout (Anderson 1983; Beard & Carline 1991; Essington et al. 1998; Rubin et al. 2004).

The reservoir influence

Our results showed two different spawning patterns: on the reaches connected with the reservoir, the spawning period lasted more than 3 months, twice as long as on the isolated reaches. The presence of spawners from the reservoir, which are significantly larger than stream residents, may partly explain the long spawning season on reaches 2 and 3: larger females are known to excavate larger redds than smaller females (Ottaway et al. 1981; Champigneulle et al. 1988; Crisp & Carling 1989) and the redd length decreased with time on reaches 2 and 3, although this accounted for very little variability. More significant was the absence of redds longer than 1.9 m after 10 March (Fig. 3.5b), suggesting that the fish which spawned on very late dates were from the stream-resident population, which is usual in salmonids (e.g., Campbell 1977; Elliott 1984; Knapp & Vredenburg 1996; Jonsson & Jonsson 1999). The slight drop in spawning activity in the second half of February suggests that there may be two waves of spawners: migrants from the reservoir spawning first, followed by the smaller stream-dwelling trout. Furthermore, larger females are known to bury their eggs deeper (van den Berghe & Gross 1984, 1989). As there is a high rate of redd superimposition, it would be logical for smaller females to spawn later to avoid having their nests destroyed by larger females, whereas the progeny of larger females would be less susceptible to damage from the superimposition of smaller spawners (van den Berghe & Gross 1984).

Therefore, the long spawning period may be partly explained by the competition for spawning space between the large reservoir and the small-resident trout. Nevertheless, there is another part of the river, isolated from the reservoir, which might have a similar spawning pattern: data were also gathered from reach 5, which is located about 1 km downstream from the lowermost sampling site (Fig. 3.1). Due to logistical constraints, reach 5 was only surveyed during January. These limited data showed that there were spawning trout in reach 5 at least from 3 January, about the same time that spawning began on reach 2. This suggests that the spawning duration on reach 5 might be similar to that on reach 2, even though there are no reservoir spawners here. However, the duration of spawning on reach 5 remains unknown, so at this point it is not possible to answer this question. It would be very valuable to identify the different population components on reaches 2 and 3 and to obtain systematic redd counts on reach 5.

Spawning period of a southern brown trout population

In any case, the shorter spawning period on reaches 1 and 4 may also be related to other factors: (i) The weak environmental constraints on these reaches can lead brown trout to concentrate their spawning, as it is more unlikely that catastrophic events would destroy the recruitment: the headwaters benefit from a constant water temperature and less drastic spates; on reach 4, the reservoir outlet provides a smoothened flow regime and similar temperatures to those in the headwaters. (ii) The small population size caused by habitat fragmentation: it has been observed by anglers and managers that smaller trout populations usually show shorter spawning durations. This highlights the problems with habitat fragmentation, and emphasizes the need for suitable spawning habitat along the river continuum.

Sampling error

There are several potential sources of sampling error in redd counts: interobserver variability, redd age, redd density and variation in fieldwork conditions due to changes in stream flow or turbidity (Dunham et al. 2001). Other problems with this method are the delineation between test-digs and actual redds and the phenomenon of redd superimposition (Al-Chokhachy et al. 2005).

In this study, interobserver variability had no influence as all the surveys were performed by the same researcher. Similarly, redd age was probably irrelevant because the observed new redds were never more than 2 weeks old. Visibility conditions were good and rather constant throughout the spawning season, except for the spate on 15 March, when the surveys were aborted until the flow subsided and the turbidity disappeared. It is possible, however, that redds built during the high flow period remained undetected. Redd density has been shown to increase the omissions of actual redds (Dunham et al. 2001), and redd superimposition may act in the same direction, so redd numbers may have been underestimated when surveying at high redd densities and high superimposition rates. However, these biases would have decreased the number of redds detected in the middle of the spawning season, especially on reaches 2 and 3, but they do not detract from our main results, namely the long spawning period and the dates in which it occurred. Finally, it is difficult to distinguish between test-digs and redds without excavation to find eggs (Dunham et al. 2001). To address this problem, any redd without an obvious tailspill (Dunham et al. 2001) or shorter than 60 cm in TL was systematically rejected in the analyses.

In conclusion, we reported here on the reproduction of a natural brown trout population close to the southern limit of the species distribution. It has shown a belated and protracted spawning period which has important implications in population management and conservation. Our findings highlight the importance of a sound knowledge of brown trout ecology and biology at this extreme latitude for which there is a critical lack of data. Further research is also needed in different predictable and unpredictable habitats to check the hypotheses proposed above and to better understand the factors that determine the duration of spawning in brown trout populations.

4. REDD SUPERIMPOSITION IN RELATION TO SPAWNING HABITAT AVAILABILITY

This chapter has been published in a peer-reviewed international scientific journal:

Gortázar, J., Alonso, C. & García de Jalón, D. (2012). Brown trout redd superimposition in relation to spawning habitat availability. Ecology of Freshwater Fish, 21, 283–292.

Redd superimposition in relation to spawning habitat availability

4.1. Introduction

Redd superimposition, i.e., spawning over a redd site where another female has previously laid her eggs, is a common phenomenon in salmonids. It has been documented in streams where different species reproduce (Sorensen et al. 1995; Landergren 1999; Taniguchi et al. 2000). Superimposition also occurs among the individuals of a single salmonid population, both anadromous (Rubin & Glimsäter 1996; Rubin et al. 2004) and stream resident (Champigneulle et al. 1988; Beard & Carline 1991). Redd superimposition can potentially damage the previously laid eggs or fry (Hayes 1987; Rubin & Glimsäter 1996; Taniguchi et al. 2000) and thus may be detrimental to population recruitment. However, it has been suggested as well that superimposition does not necessarily destroy the previous nests (Anderson 1983; Gortázar et al. 2007; Weeber et al. 2010).

High numbers of spawners in relation to suitable spawning habitat has previously been cited as the reason for redd superimposition (Champigneulle et al. 1988, 2003; Beard & Carline 1991; Rubin et al. 2004). Conversely, Taggart et al. (2001) found that redd superimposition was not correlated with the number of spawners. Furthermore, Essington et al. (1998) showed that female density alone cannot explain redd superimposition and concluded that brown trout (Salmo trutta L.) and brook trout (Salvelinus fontinalis Mitchill) females prefer to spawn over redd sites previously used by another female. They argued that the presence of an existing redd makes a particular site more attractive for spawning than it would otherwise be.

Our study was conducted in a limestone stream where processes of calcium carbonate precipitation can potentially make the substrate very cohesive and difficult to dig. Thus, it is plausible that the reuse of redd sites in this river would be a great advantage. The objective of this study was to determine, at the microhabitat scale, whether spawning habitat availability can explain redd superimposition or whether other factors such as female preference for reusing redd sites are responsible.

4.2. Material and methods

Study area

The study was performed in the upper reaches of the river Castril, a limestone, aquifer- fed stream, located in the province of Granada (Andalusia, south-eastern Spain). Brown trout is the only fish species inhabiting the study area, and this population is close to the southern limit of the natural distribution of the species (Elliott 1994). This is a native population, and it has no introgression of foreign trout alleles (Almodóvar et al. 2007). This brown trout population has a protracted and belated reproductive season (Gortázar et al. 2007), which ranges from December to mid-April, with maximum spawning activity in February. For further details on the river Castril and its brown trout population, see Gortázar et al. (2007).

Redd superimposition in relation to spawning habitat availability

The river Castril is divided into three separate reaches by impassable obstacles. These reaches, listed from upstream to downstream, are as follows (Gortázar et al. 2007): (i) the headwaters, which is separated from the rest of the downstream river by a 500-m- long reach where the river becomes subterranean, owing to flow detraction, (ii) the main stem of the stream, which is connected to the “El Portillo” reservoir and it is also separated from downstream by its dam and (iii) the reach below the reservoir, which is also isolated from the rest of the river upstream and downstream. Given this situation, we defined two different brown trout groups in this river: the populations inhabiting the reaches (i) and (iii), which are stream-resident trout and which will be called “group A” from now on, and the population in reach (ii) where stream-dwelling trout coexist with larger-sized reservoir-migrant trout (“group B”).

Redd surveys

Twenty-four sections within the river Castril were selected as they were known to be frequently used by spawning brown trout. Twenty-one sections are in the part of the river which is connected to the reservoir (group B). Only three sections were selected in the isolated reaches (group A) because spawning habitat is less abundant there and it was not possible to find more appropriate places.

During the spawning season 2004–2005, all the sections were visited every week to monitor the spawning activity by means of redd counts. At the end of the season (March and April), the sections were visited every other week. The methodology for identifying new and superimposed redds is described in detail in Gortázar et al. (2007), but here is a summary. Redds were identified by their configuration, with a pit upstream followed by a dome downstream, and by the lack of periphyton over the substrate, caused by female digging activities (Crisp & Carling 1989; Grost et al. 1991). Excavations without an obvious dome or that were shorter than 60 cm in total length were not considered redds. The latter condition was based on the relationships between redd tail length, redd horizontal dimensions and fish length given by Crisp & Carling (1989). Every time a new redd was detected, its horizontal dimensions were measured (length and width of pit, dome and entire redd), a sketch was drawn, and several photographs were taken. The redd position was marked on an aerial photograph, and its coordinates were recorded with GPS. For conservation reasons, we did not excavate any redds to check for the presence of eggs, but several of them were positively confirmed as redds by observing spawning trout over them.

Every redd that had been recorded in a previous survey was checked to determine whether it had changed owing to superimposition or remained unchanged. These checks were based on the comparison of the redd horizontal dimensions, sketch and photographs, between the previous and the current survey. If the redd had increased in size, or if the position and size of pit and dome had changed, we considered that a superimposed redd had been constructed over the pre-existing one (Gortázar et al. 2007). In this case, new measurements, photographs and a sketch were made. When it was not clear whether an individual redd had changed or not, we considered it unchanged (i.e., no superimposition had occurred). In summary, we considered a new individual redd to be any new continuous area of dug up gravels with a pit and a dome that was longer than 60 cm. A superimposed redd was considered to be (i) any pre- existing redd that had been significantly enlarged and (ii) any pre-existing redd that showed different pit or dome horizontal dimensions.

Redd superimposition in relation to spawning habitat availability

Water clarity, river gradient and width (ca. 8 m) enabled good visibility and working conditions during fieldwork, and the stream flow was moderate (ca. 1 m3/s) during the spawning season (Gortázar et al. 2007). All the redd surveys were performed by the same researcher, walking along the riverbed or riverbank. Frequent visual verifications were made to ensure that the substrate was not altered by the surveyor.

Suitable Spawning Habitat (SSH)

Sites used by brown trout females for spawning in the river Castril were characterised in a subsample of redds (N = 30) at the microhabitat scale. All the parameters previously considered to be important in redd site selection were measured: water depth, mean water column velocity, bottom water velocity, surface substrate size and substrate embeddedness (Shirvell & Dungey 1983; Witzel & MacCrimmon 1983; Essington et al. 1998, among others). We did not assess groundwater upwelling because, although it is an important parameter in redd site selection in several salmonid species (Curry & Noakes 1995; Garrett et al. 1998; Baxter & McPhail 1999; Baxter & Hauer 2000), it is not as relevant in brown trout spawning (Witzel & MacCrimmon 1983; Essington et al. 1998). Depth was measured with a graduated rod. Water velocities were measured with a Valeport Model 801 electromagnetic flow meter (flat sensor type): mean water column velocity at 60% of the water depth and bottom velocity at 3 cm over the gravel surface. The dominant surface substrate was measured in the field and classified into one of eight possible categories: silt (<1 mm), sand (1–4 mm), fine gravel (4–10 mm), medium gravel (10–30 mm), coarse gravel (30–75 mm), cobble (75– 300 mm), boulder (>300 mm) and bedrock. Following the study of Essington et al. (1998), substrate embeddedness was classified into three categories: (i) there are little or moderate fine elements, and the substrate can be easily dislodged with the hand; (ii) there are many fine elements, and the substrate can be dislodged with the hand with a moderate effort; and (iii) a big effort is needed to separate the gravel. These five variables were measured at three points on undisturbed gravel around the redd pit: at the upstream end and at both sides of the pit. An area of ca. 10 cm radius around each point was evaluated for substrate size and embeddedness. For each variable, the three measurements were averaged to give an estimate of the microhabitat value at the site where the redd was built (Crisp & Carling 1989; Champigneulle et al. 2003). To describe the habitat available to spawning females, we also measured depth, mean velocity, bottom velocity, substrate size and embeddedness at 214 randomly selected points within the studied sections.

To evaluate microhabitat selection by spawning brown trout, we first performed Kolmogorov-Smirnov two-sample tests (Zar 1999) to compare the distributions of available and used values for the variables depth, mean velocity, bottom velocity and substrate size. We then used the Strauss linear electivity index (Strauss 1979) to determine which values of these variables are positively selected by spawning females. For index calculation, values of each variable were divided into classes, using 10-cm increments for depth and 15-cm/s increments for mean and bottom velocity. The Strauss electivity index is defined as: L = ri – pi where ri and pi are the relative abundances (expressed as proportions) of used and available habitat, respectively, for the habitat class i. The t-statistic was used to test whether electivity indices (L) were significantly greater than zero at the 99% significance level (P < 0.01). The classes in which L is significantly greater than zero were considered to be positively selected by spawners.

Redd superimposition in relation to spawning habitat availability

These classes were grouped together into ranges so that we had a defined range for positive selection for each of the four variables. These ranges were used to define suitable spawning habitat (SSH). A particular point in the river was considered SSH if the four microhabitat variables had values within the ranges considered suitable for spawning and had class 1 embeddedness (loose gravel with little or moderate fine elements, Stuart 1953; Beard & Carline 1991).

All the values of mean and bottom velocity which were significantly selected at the 95% level were considered suitable for spawning. We relaxed this criterion for surface substrate size by allowing some disperse stones of other sizes to be present within the SSH. For water depth, as well as the significantly selected values, we also included all values up to the maximum used depth. These depths were included because it seems quite likely that the upper limit for spawning depth arises from the limited availability of greater water depths with appropriate current and from the inability of research workers to detect deep redds (Crisp & Carling 1989).

Therefore, the SSH satisfies the brown trout requirements for depth, mean water velocity, bottom velocity, substrate size and embeddedness. Because the SSH meets the five requirements, corresponding to the five microhabitat variables, we assumed that SSH represents good-quality spawning habitat. Nevertheless, females may actually spawn in less suitable habitats as well, e.g., in sites that only meet three or four requirements.

Within each section, we identified a single continuous area that satisfied the five microhabitat requirements. This polygon was considered the SSH for brown trout in the section, and its area was then measured. Following Delacoste et al. (1993), only the largest continuous area of SSH within the section was considered, while smaller separate patches were ignored.

Redd superimposition

Two analyses were performed to investigate whether redd superimposition was caused by a high density of spawners relative to the available spawning habitat or not. First, simple regression analyses were performed to determine whether the percentage of superimposed redds was correlated with redd numbers, SSH or redd density (number of redds divided by SSH) in each section. As the actual number of spawners is unknown, redd density was used as a surrogate of spawner density.

Second, following Essington et al. (1998) study, a simulation-based analysis was carried out to compare the observed number of superimposed redds versus the expected if redd sites were randomly distributed within the SSH, given the actual number of redds and their area. In each section, we estimated the expected number of superimposed redds if they were randomly placed within the SSH, assuming that all redds were completely inside the SSH. The redd area used in the simulations was the average area of the nonsuperimposed redds (new redds). Mean redd area was calculated separately for each part of the river, resulting 0.76 m2 (SD = 0.38 m2) for group A and 0.87 m2 (SD = 0.59 m2) for group B. In the simulations, we considered that one redd was superimposed if it overlapped another by more than 25% of its area. When several redds were overlapped in a simulation, we considered that only one redd was not superimposed, so the number of superimposed redds was considered to be the number of overlapped redds minus one

Redd superimposition in relation to spawning habitat availability

(the first one constructed). 1000 simulations were performed in each section, and the resulting mean and 0.05 and 0.95 percentiles of the expected number of superimposed redds were calculated. If the observed number of superimposed redds exceeded the 0.95 percentile of the simulated superimposition, we concluded that redd superimposition was significantly higher than that expected if redd sites were randomly selected.

4.3. Results

Both parts of the river Castril provided very different results. In the part of the stream connected to the reservoir (group B), 127 redds were detected, and 71 redds were superimposed over previously constructed redds, yielding a rate of redd superimposition of 55.9%. In the part of the stream that did not have reservoir-migrant trouts (group A), the total redd count was 18 and 6 superimposed redds were detected, a superimposition rate of 33.3%.

Suitable Spawning Habitat (SSH)

The distributions of available and used microhabitat were markedly different for the four variables: depth (Kolmogorov-Smirnov two-sample test, D = 0.27, P < 0.05), mean water column velocity (K-S test, D = 0.60, P < 0.01), bottom water velocity (K-S test, D = 0.67, P < 0.01) and surface substrate size (K-S test, D = 0.69, P < 0.01). Female brown trout showed significant positive selection (P < 0.01) for depths between 10 and 30 cm, mean velocities between 30 and 60 cm/s, bottom velocities between 15 and 60 cm/s and substrate sizes ranging 4–30 mm (Fig. 4.1).

0.6 Available 0.4 0.5 * Used 0.3 * 0.4 * 0.3 * 0.2 0.2 0.1 0.1 0 0 10 20 30 40 50 60 70 80 90 100 15 30 45 60 75 90 105 120 135 Depth (cm) Mean velocity (cm/s) 0.6 * 0.5 * Proportion 0.5 0.4 0.4 * * 0.3 0.3 * 0.2 0.2 0.1 0.1 0 0 1 4 10 30 75 300 Bed- 15 30 45 60 75 90 105 120 135 rock Surface substrate size (mm) Bottom velocity (cm/s) Figure 4.1. Distribution of available (solid bars) and used (open bars) values of depth, mean water column velocity, bottom water velocity and substrate size. The asterisk indicates a significant Strauss electivity index (P < 0.01).

Redd superimposition in relation to spawning habitat availability

Although only depths between 10 and 30 cm were significantly selected by females, values >30 cm up to the highest used depth (50 cm) were considered suitable as well (see Methods for details). Note that the depth class 30–40 cm was not significantly avoided by females (P > 0.05), and in the class 40–50 cm use exceeded availability.

In summary, every site within the SSH must have depth between 10 and 50 cm, mean water velocity between 30 and 60 cm/s, bottom velocity between 15 and 60 cm/s, surface substrate size in the range 4–30 mm and embeddedness class 1. The SSH area measured in each section ranged from 10.5 to 90.8 m2.

Redd superimposition

Among the studied sections, observed superimposition rates ranged from 0% in section 8, where only two redds were constructed and none of them were superimposed, to 83.3% in section 12, where five of the six constructed redds were superimposed (Table 4.1). Overall, the average superimposition rate for the studied sections in the river Castril was 53.1% (77 superimposed out of 145 redds).

Table 4.1. Observed number of redds and superimposed redds (percentage in brackets) in each section. Observed Observed Observed Observed Section number of superimposed Section number of superimposed redds redds redds redds 1 4 1 (25) 4 10 6 (60) 2 4 3 (75) 5 5 1 (20) 3 10 2 (20) 6 10 5 (50) 7 5 1 (20) 8 2 0 (0) 9 3 1 (33.3) 10 3 2 (66.7) 11 6 3 (50) 12 6 5 (83.3) 13 8 5 (62.5) 14 7 4 (57.1) 15 4 3 (75) 16 2 1 (50) 17 7 4 (57.1) 18 4 1 (25) 19 10 8 (80) 20 5 3 (60) 21 12 7 (58.3) 22 8 5 (62.5) 23 7 5 (71.4) 24 3 1 (33.3) Group A 18 6 (33.3) Group B 127 71 (55.9)

Simple regression analyses showed that the percentage of superimposed redds was not correlated with either redd numbers (regression analysis: R2 = 0.103, P = 0.13, N = 24) or available SSH area (R2 = 0.001, P = 0.9, N = 24). Moreover, superimposition and redd density (number of redds per SSH area) were also not correlated (R2 = 0.096, P = 0.14, N = 24, Fig. 4.2). Significance was not improved by analysing group B (R2 =

Redd superimposition in relation to spawning habitat availability

0.063, P = 0.27, N = 21) or group A alone (R2 = 0.868, P = 0.24, N = 3). Furthermore, the sections with high redd density had high rates of redd superimposition, but the sections with low redd density showed a highly variable superimposition, ranging from 0 to more than 80% (Fig. 4.2).

100

40 Superimposition (%)

0 0 102030405060708090 2 Redd density (number/100m SSH) Figure 4.2. Relationship between redd superimposition and redd density in 24 sections within group A (open circles) and group B (solid circles).

Almost all the sections (23 of 24) showed a higher rate of redd superimposition than expected if redds were placed randomly (Fig. 4.3a). The exception was section 8, which is the only section where no redds were superimposed. In eight sections, the difference between observed and expected superimposition was significant (P < 0.05). Furthermore, in another eight sections, the observed number of superimposed redds equalled the 0.95 percentile of the expected random superimposition, thus being right at the limit of the statistical significance (P = 0.05).

The rate of redd superimposition was significantly higher than expected if redds were placed randomly both after the aggregation of all the sections of the stream and among the reaches connected to the reservoir (group B). Regarding the part of the stream isolated from the reservoir (group A), redd superimposition was also higher than expected, but without statistical significance at the 95% s.l. (Fig. 4.3b).

Redd superimposition in relation to spawning habitat availability

10 (a) 9 10** 8 12** 7 10** 6 10 6** 8** 87* 5 7 7** 4 4* 6** 4* 5** 3 10 3* 2 4 5 5 3* 2* 4* 3* 1 2 0

Isolated (A) Connected (group B)

145 ** 80 (b) 127 ** 60 Number of superimposedredds 40

20 18 0 Group A Group B Whole (Isolated) (Connected) stream

Figure 4.3. Observed number of superimposed redds (solid bars) and mean expected superimposition if redds were randomly placed over the suitable spawning habitat (SSH) (open bars). Lines denote 0.05 and 0.95 percentiles of the expected number of superimposed redds, calculated by performing 1,000 simulations of random redd location over the SSH. The total number of observed redds is shown above bars. (a) Results for each section and (b) aggregated results. **Significant difference at the 95% s.l. (P < 0.05) between expected and observed superimposition. *The difference is right at the limit of statistical significance (P = 0.05).

4.4. Discussion

The rate of redd superimposition in the part of the river Castril connected to the reservoir (group B) was 55.9%, close to the highest values found in the literature: Anderson (1983) observed percentages of superimposed redds higher than 50%, and Essington et al. (1998) reported that between 18% and 50% of brown trout females constructed their redds over preexisting ones. In a sea trout population, Rubin et al. (2004) observed that 60% of the spawning area was used by at least two different pairs. In the isolated reaches (group A), the superimposition rate was lower (33.3%) but non- negligible in comparison with the values from other streams (Anderson 1983; Essington et al. 1998).

Suitable Spawning Habitat (SSH)

Suitable spawning habitat (SSH) has been defined as the continuous area inside which every point satisfies five requirements, one for each microhabitat variable: depth, mean water column velocity, bottom water velocity, substrate size and embeddedness. We are

Redd superimposition in relation to spawning habitat availability confident that SSH represents good-quality spawning habitat for brown trout in the river Castril, because it takes into consideration microhabitat characteristics that are positively selected by females in this river. Furthermore, only one continuous SSH area has been used within each section, while smaller separate patches have not been considered in the analyses (Delacoste et al. 1993).

Several factors illustrate that the criteria used for defining the SSH are demanding. First, it is known that spawning brown trout choose locations with optimum combinations of several microhabitat variables rather than positions with more preferred levels of any single factor (Shirvell & Dungey 1983). Because of this, we imposed the criterion of satisfying the five microhabitat requirements, and it was not possible to compensate an unsuitable value in one variable by high suitability values in other variables. Second, we decided to use both mean velocity and bottom velocity in the definition of SSH, despite the fact that only the latter has been suggested to have biological significance: females actually sense the bottom velocity when they choose a place to spawn, because it determines the hydraulic conditions around the nest that will act during embryo development (Shirvell & Dungey 1983; Delacoste et al. 1995). Both water velocities were considered in order to impose demanding criteria for SSH and thus to assure that it represents high-quality spawning habitat. Third, only embeddedness class 1, which is the most favourable type for redd digging (gravel loose and with little fine elements), has been considered suitable for spawning.

Table 4.2. Brown trout selection for spawning microhabitat in several works. Two criteria are shown, one more demanding and another more relaxed. Notes in the references relate to methodology explanations. Mean Bottom Substrate Reference Criterion Depth (cm) velocity velocity size (mm) (cm/s) (cm/s) This study 10–30 30–60 15–60 4–30 (Grost et al. 1990) 1 Selection 12.3–18.3 24.5–36.6 No Use > av. 12.3–30.5 24.5–73.2 7–75 (7–25)* (Bovee 1978) 2 PU > 0.9 15–25 45–65 2–20** PU > 0.5 10–35 30–75 (Raleigh et al. 1986) 3 SI = 1 > 25 20–50 6–76 SI > 0.5 > 15 15–85 4–80 (Delacoste et al. 1995) 4 HI > 0.9 15–25 25–30 2–100 HI > 0.5 10–30 15–35 (Mayo Rustarazo et al. 1995) 2 PU > 0.9 15–28 20–35 12–25 10–30** PU > 0.5 8–42 13–50 5–37 1 Comparison between available and used habitat by means of the Strauss electivity index: Use > av.: habitat used in a greater proportion than its availability; Selection: significant selection at the 95% s.l. * For the substrate range 7–75 mm, use exceeded availability. The difference was most marked in the range 7–25 mm. 2 PU, probability of use, ranges from 0 to 1. ** PU = 1. 3 Category one curves (based on literature or professional opinion), SI, suitability index. 4 HI, habitat suitability, ranges from 0 (not suitable) to 1 (optimal).

Redd superimposition in relation to spawning habitat availability

The selection of spawning habitat may exhibit regional and river-specific characteristics (Delacoste et al. 1995). For this reason, we analysed the values of each microhabitat variable that were selected by female brown trout in the river Castril, and we used these particular values to compute the SSH. Anyway, our results on female selection for spawning microhabitat characteristics are consistent with previous studies on habitat selection, despite some minor differences (Table 4.2). Grost et al. (1990) also used the Strauss electivity index to investigate habitat selection and reported narrower significant selection ranges for depth and mean velocity, but the intervals in which habitat use exceeded availability did include the suitable values obtained by us. Grost et al. (1990) did not observe any significant selection for substrate size, although the interval with highest use in relation to its availability was similar to our suitable values for this variable.

The suitable ranges obtained in our study were similar to the majority of the reviewed values from several authors (Table 4.2). Interestingly, Raleigh et al. (1986) did not report any upper limit for depth, which supports our decision to consider the range 10– 50 cm as suitable. The biggest disparities between our data and the literature were for the bottom velocities given by Delacoste et al. (1995) and Mayo Rustarazo et al. (1995). However, these authors reported rather lower velocities than those used for spawning in several studies (Shirvell & Dungey 1983; Plasseraud et al. 1990; Sorensen et al. 1995). Thus, we are confident that the ranges we used as suitable to compute the SSH were correct and represented high-quality spawning habitat in the river Castril.

Spawning habitat influence on redd superimposition

Several authors have supported that redd superimposition in salmonids is caused by high density of spawners, i.e., an excess of reproductive females and/or a scarcity of suitable habitat (Champigneulle et al. 1988, 2003; Beard & Carline 1991; Rubin et al. 2004). If redd superimposition was caused by habitat availability alone (limited suitable habitat or excess of spawners), a positive correlation between superimposition rate and redd density would be expected. We did not find any significant relationship between these variables in Castril. Our simple regression analysis showed high superimposition rates at high redd densities, but at lower redd densities, superimposition was highly variable (Fig. 4.2).

Obviously, if the number of spawners is too high in relation to available spawning habitat, superimposition will often occur, because there is no more space for reproduction. Thus, a minimum superimposition rate can be determined by redd density in the form of a linear function. In this sense, a given density may impose a superimposition rate over which female spawners can increase the rate but not decrease it. So, if the density of spawners is low (and thus spawning habitat is not limiting), a high degree of superimposition suggests that the availability of spawning habitat is not the sole cause of superimposition. For instance, in many sections of the river Castril, we observed low redd density and high superimposition rates.

Moreover, the results of the simulation-based analysis showed that, in the majority of cases, the observed superimposition is higher than that expected if redd sites were randomly placed over the SSH. This fact strongly suggests that females choose specific sites for redd construction instead of randomly dispersing over the available quality habitat. The difference between observed and expected superimposition is less clear in

Redd superimposition in relation to spawning habitat availability the isolated reaches (group A) than in the part of the river connected to the reservoir (group B), but it is not possible to draw any conclusions from this, because we only had data from three sections within group A. However, even in group A, the observed superimposition was higher than would be expected by chance. Nevertheless, superimposition rate was lower in group A than in group B, and this may be related to differences in spawning behaviour or female body size: it is known that spawners from group B are larger than those from group A owing to the presence of reservoir-migrant trout (Gortázar et al. 2007), but again, the lack of data from group A prevents us from drawing any conclusions about this issue.

At this point, it is convenient to make an observation about two methodological factors related to the simulation-based analysis, which reduced the probability of achieving our result that the observed superimposition was greater than the expected. These factors act by underestimating the observed superimposition or by overestimating the simulated superimposition: (i) during fieldwork, every time it was not clear whether an individual redd had been superimposed or not, we considered that no superimposition had occurred, and this may underestimate the observed superimposition. (ii) Furthermore, when several redds were overlapped in a simulation, we considered that all the redds except one (the first one constructed) were superimposed. This may overestimate the simulated superimposition, because it is possible that not one but two or more redds were first built without superimposition and later a single redd overlapped several of the previous redds.

We believe that the most likely explanation for the high superimposition rates, even at low spawner density, is that brown trout females have some kind of preference to spawn over pre-existing redd sites, as showed by Essington et al. (1998) in a behavioural experiment. These authors argued that the presence of an existing redd makes a particular site more attractive for spawning than it would otherwise be, because the female may reduce the energetic cost of reproduction and increase subsequent fitness. Youngson et al. (2011) confirmed the finding that females tend to spawn close to locations that have already been used. Therefore, the modification of stream substratum by prior spawners must be regarded as a factor that affects spawning site selection in brown trout.

It is important to underline that our findings refer to the microhabitat scale at which this study has been conducted: Females may exhibit a preference to spawn over a previously used redd site, but this only occurs after the choice of a particular reach within the accessible part of the stream and once the female has decided on a particular streambed patch within the reach. These decisions are largely determined by the suitability and availability of spawning habitat (e.g., Shirvell & Dungey 1983; Beard & Carline 1991; Delacoste et al. 1993).

Regarding the outcome of redd superimposition, several authors have supported the idea that it may negatively impact spawning success in salmonid populations by damaging the previously laid eggs or fry (Hayes 1987; Rubin & Glimsäter 1996; Taniguchi et al. 2000). However, Anderson (1983) observed that the contents of pre-existing redds are not necessarily destroyed when superimposition occurs. Weeber et al. (2010) found that redd superimposition did not affect bull trout (Salvelinus confluentus Suckley) egg-to- fry survival owing to the shallower scouring by the following kokanee (Oncorhynchus nerka Walbaum) spawners. In the river Castril, Gortázar et al. (2007) suggested that the

Redd superimposition in relation to spawning habitat availability timing of brown trout spawning, which extends from December to April, may reduce the potential damage of redd superimposition: Smaller females seem to spawn later in the season, thus avoiding having their nests destroyed by larger females, which are known to bury their eggs deeper (van den Berghe & Gross 1984, 1989). In this way, redds of larger females would be less susceptible to damage from the superimposition of smaller spawners which bury their eggs shallower.

Furthermore, in limestone streams such as the Castril, the chemical process of calcium carbonate precipitation can potentially embed the gravel, making it cohesive and difficult to excavate. It seems plausible that the advantage of reusing a redd site would be greater in rivers with this type of substrate, where the gravel was very cohesive and thus the substrate was highly embedded in unused sites. It would be valuable to address this hypothesis by the development of a comparative study between rivers with different degrees of substrate embeddedness, to analyse whether the superimposition preference is higher in streams with more embedded substrate or not. More research is also needed to fill the critical lack of data on the ecology of the southernmost brown trout populations, to manage them better.

5. MICROHABITAT SIMULATION OF INSTREAM HABITAT ENHANCEMENT OPTIONS

This chapter has been published in a peer-reviewed international scientific journal:

García de Jalón, D. & Gortázar, J. (2007). Evaluation of instream habitat enhancement options using fish habitat simulations: case-studies in the river Pas (Spain). Aquatic Ecology, 41, 461–474.

Microhabitat simulation of instream habitat enhancement options

5.1. Introduction

Northern coastal rivers of Spain maintain Atlantic salmon (Salmo salar L.) populations, being the most southern localities along their world distribution range, although being on threatened status (García de Jalón 1997). Frequently, fish habitat in these rivers is quite degraded because of human activities. In this study, the effectiveness of some habitat improvement measures was investigated, to reveal if it is useful to implement these actions.

Fish stocks can be limited by food, refuge, spawning habitat, flow fluctuations and water quality constraints but also by factors other than habitat such as biological interactions including competition, predation and parasitism. Previous to any Physical Habitat Improvement implantation one should check which of these factors is acting as a bottleneck for the fish population we want to enhance.

Improving physical habitat conditions for fisheries should take into account natural recovery processes (Cairns et al. 1977; Gore 1985; Reice et al. 1990) and the biogenic capacity of the reach, in order to act along with nature (Heede & Rinne 1990) rather than against it, allowing nature to improve the stream by itself (White & Brynildson 1967).

In order to maintain habitat traits best adapted to target fish population needs, different nonstructural and structural approaches have been proposed (Hey 1994; García de Jalón 1995). Structural measures often include different types of weirs and deflectors. The design and best implementation of some of these structures in alluvial rivers has been outlined by Rosgen (1996) from the geomorphological point of view. These structures change local hydraulic conditions and thus, their erosion and deposition dynamics. Consequently, its substrate and microtopography is modified and therefore their habitat characteristics.

Several types of structures are used to improve fish habitat in rivers: current deflectors, low dams, boulders, half-logs, bank overhangs and others are described in literature (García de Jalón 1995). Shamloo et al. (2001) developed a study on the flow and erosion around some of these simple habitat structures, employed to provide feeding and resting areas for fish.

Deflectors are the most frequently used structures for habitat enhancement. They change the stream direction with the aim to protect the river margins, to dig pools, to concentrate water in summer or to create rapids (González del Tánago & García de Jalón 2001). For their construction, Wesche (1985) gives technical details. Usually, a series of alternate triangular deflectors is placed. Each deflector produces an accumulation of materials downstream, and it also changes the flow direction creating a pool near the other river margin. They are designed in a triangular shape with its longest side strongly fastened in the river border. According to White and Brynildson (1967), deflectors must not exceed the summer water level more than 25 cm in a year with normal rains.

Microhabitat simulation of instream habitat enhancement options

Low dams are usually introduced in a riverbed to create or to deepen pools, and also to collect gravel, which will improve the natural redds in hard slope streams. These dams are built with a “spillway” to enable migratory fish to pass at a low water level. The dam considered in this study is not straight but in V-shape, to concentrate flow at the centre and to protect better the lateral anchorages (Reeves et al. 1991). This structure accelerates water when it passes over it, and this high speed of the water digs a pool downstream. It accumulates material upstream, as well.

Many enhancement projects have been recently implemented in Spain (Schmidt & Otaola-Urrutxi 2000). The aim of these projects was to improve the stream habitat by improving the stream bed complexity. Their goal was mainly to improve suitable habitat for stream salmonids and for the macrobenthic communities on which they feed. However, no previous analysis of their potential effectiveness was carried out, neither any surveillance studies of the effects after their implementation.

It is frequently assumed among fisheries manager practitioners that the implementation of instream habitat improvement measures, such as current deflectors, always benefit fish habitat. Since this statement is not true in every case, a theoretical study was developed in this work, to assess the effectiveness of habitat enhancement measures in a particular case. Therefore, the objective of this paper is to research and evaluate, by means of habitat simulation, if the introduction of some types of structures in the riverbed, will effectively improve the useable habitat for salmon, and to quantify its influence.

Figure 5.1. Location of river Pas and the studied reach.

An upper reach of the river Pas was selected to develop this study, in the north of Iberian Peninsula, province of Cantabria, near the town of Puente Viesgo (Fig. 5.1). River Pas is a chalky stream, which flows in north direction and discharges its water in the Cantabric Sea, open to the Atlantic Ocean. Atlantic salmons use to ascend this river

Microhabitat simulation of instream habitat enhancement options for reproduction. Other fish species present in the river is brown trout (Salmo trutta L., García de Leániz & Verspoor 1989). Also minnow (Phoxinus phoxinus L.) has been recorded in this reach.

Pas is a mountain river with high slopes and a great predominance of gravel and cobble in the substrate. The hydrological regime has its absolute maximum in December and a relative maximum in April, as shown in Fig. 5.2. The river is prone to low flows at the end of summer, aggravated by water extractions for the supply of Santander city. Downstream the study site, there are several obstacles to salmon migration though they have fish passes. The nearby villages also discharge their water in the river. This impact is more serious in summer, when the stream flow is at minimum.

20 /s) 3 15

10 Discharge (m 5

0 Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Figure 5.2. Natural regime. Calculated from daily flow data from the period 1965–1997 recorded at the gauging station no. 215, located in the town of Puente Viesgo (750 m downstream the study reach).

5.2. Materials and methods

The present study evaluates the habitat improvement reached by different stream habitat enhancement options, by means of the Instream Flow Incremental Methodology (IFIM), which has been developed by the U.S. Fish and Wildlife Service and amply described in Bovee (1982), Stalnaker et al. (1995) and Bovee et al. (1998). This methodology is based on habitat characterization with the aim of see, considering the habitat requirements of some species or development stages, and by means of a hydraulic simulation, how habitat use changes depending on the stream flow variations.

IFIM can be used for determination of ecologically acceptable discharges, in order to minimize ecological impacts caused by hydraulic structures. By means of IFIM it is possible to carry out hydrological analyses, prediction of several scenarios and determination of limits for water (Caletková & Komínková 2004). From the biological point of view, IFIM allows to compare useable habitat in different conditions, thus it is possible to predict the success of any particular physical habitat improvement measure.

For this work a two-dimensional hydraulic model was chosen, because these models are very useful in studies where the detailed local distribution of depths and velocities is

Microhabitat simulation of instream habitat enhancement options important (Steffler et al. 2000). Two-dimensional approaches have been used in several stream habitat studies (Leclerc et al. 1995; Ghanem et al. 1996; Crowder & Diplas 2000, 2002; Vehanen et al. 2003).

We have employed the software River2D (Steffler 2000; Steffler & Blackburn 2002), developed by Peter Steffler at the University of Alberta, in Canada. This program uses a two dimensional hydraulic model, which simulates the hydraulic conditions (average depth, water mean velocity and direction, water surface elevation, etc.) in the surface of the studied reach, and estimates the potential value of stream habitat for the requirements of different species and development stages by Weighted Useable Area (WUA).

The River2D model was developed specifically for use in natural streams and rivers. It is a Finite Element model, based on a conservative Petrov-Galerkin upwinding formulation. It features subcritical-supercritical and wet-dry area solution capabilities. A complete description of the formulation and implementation of the model is contained in Ghanem et al. (1995). The implementation of habitat requirements is done in a similar way as it is done in Phabsim one-dimensional model.

To carry out the simulation with this software it is necessary to have the riverbed topography, and to know the flow magnitude and the inflow and outflow water elevation. To estimate the Weighted Useable Area the program also need to know the preference of depth, current velocity and substrate type of all the development stages of the fish that are considered.

Using an Electronic Total Station PENTAX PCS-315 and an appropriate prism, the riverbed topography was measured at four reaches of around 100 meters long each in a one kilometer river segment. Among these reaches, the one which best represents the river segment was selected to carry out the habitat simulation. 395 topographical spots were surveyed within the river reach bed. The registered data were the X and Y coordinates and the bed elevation in meters, along with the composition of the substrate in the bed, according to the key showed in Table 5.1. It is important to take a good measurement of the river margins to know the water surface elevation when the fieldwork was done; this elevation, along with the stream flow at the same moment, will be the boundary conditions needed by the software to carry out the simulation.

Table 5.1. Types of substrate considered and their effective roughness height (in meters). Substrate type Roughness (m) 1. Silt and fine materials (<0.6 mm) 0.01 2. Sand (0.6–3 mm) 0.05 3. Gravel (4–9 mm) 0.03 4. Cobble (10–300 mm) 0.07 5. Boulder and submersed logs (>300 mm) 0.7 6. Bed rock 0.1

The “effective roughness height” was chosen as the resistance parameter for the hydraulic model, because it tends to remain constant over a wider range of depth than roughness coefficients do. Compared to traditional one-dimensional models, where

Microhabitat simulation of instream habitat enhancement options many two-dimensional effects are abstracted into the resistance factor, the two- dimensional resistance term accounts only for the direct bed shear. Observations of bed material and bedform size are usually sufficient to establish reasonable initial roughness estimates. For resistance due primarily to bed material roughness, a starting estimate of effective roughness height was taken as 2–3 times the largest grain diameter. The final values were obtained by calibrating the model results to measured water surface elevations and velocities.

Instream flow magnitude was estimated in two transverse channel sections, one upstream and another downstream. Along each transverse section a series of points were located where a significant change in the slope, substrate type, water velocity, depth, etc. was found. In each point, its distance to the right margin was measured; depth was also noted down, employing a graduated stick; finally, water velocity at a distance from the riverbed of 0.6 times the depth was recorded, using an Electromagnetic Flow Meter VALEPORT 801. With these data flow magnitude in the reach was obtained.

1 Adult 0.8 Parr 0.6 Fry

0.4

0.2

0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Water velocity (m/s)

Figure 5.3. Average water velocity preference curve for Atlantic salmon.

Adult 0.8 Parr 0.6 Fry

0.4

0.2

0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Depth (m)

Figure 5.4. Water column depth preference curve for Atlantic salmon.

Microhabitat simulation of instream habitat enhancement options

0.8 Adult Parr 0.6 Fry

0.4

0.2

0 Silt Sand Gravel Cobble Boulder Bed rock

Figure 5.5. Substrate preference for Atlantic salmon.

The suitability of three hydraulic variables (current velocity, depth and substrate type) have been considered, and three Atlantic salmon development stages have been studied: adult, parr and fry. Their preference curves (Figs. 5.3–5.5) are based in those developed by Heggenes (1990a), but modified taking into account our field observations in river Pas.

These data allow to obtain the relation between discharge and potential habitat for each salmon development stage, by means of the hydraulic simulation. The boundary conditions required by the hydraulic model to execute the simulation are the stream flow at the upstream cross-section and the water surface elevation at the downstream and upstream cross-sections. With these data, the software is able to adjust the following expression: · where “q” is flow per unit of width (m2/s), “d” is the water height in the downstream section and “a” and “b” are two constants which are adjusted according to stream flow and water height data measured in the field.

The Weighted Useable Area (WUA) is the surface (m2) that can be potentially used, with a maximum preference, by the considered species or development stage. The study of Weighted Useable Area allows to know how a species can use the river habitat depending on the stream characteristics and flow variations.

Firstly, the habitat simulation was carried out for the stream reach under unmodified conditions. Next, three alternate triangular deflectors were supposed to be in the riverbed, and the simulation was done again, to see how the stream habitat changed. Another improvement measure was supposed: the introduction of a low dam in a narrowing of the studied reach. The simulation was carried out once again for the low dam case. By comparing habitat in the control conditions with habitat obtained with the deflectors and the low dam, the effectiveness of each improvement measure was assessed. This comparison also shows the instream flow environment at which maximum improvement is obtained.

Afterwards, the bed topography was changed considering the geomorphological adjustments due to the new erosion and sedimentation areas caused by the presence of the improvement structures. These adjustments on the erosion zones were estimated on

Microhabitat simulation of instream habitat enhancement options the basis of an analysis of cells that had maximum shear stress values at high flows, while the sedimentation ones were evaluated at minimum shear stress cells. The quantification of these adjustments was estimated based on expert knowledge. These adjustments determined a modified riverbed topography that was introduced in River2D model. We have performed new simulations again for this topography in order to investigate their habitat changes and to quantify their potential habitat improvement.

With the aim to evaluate the habitat increase obtained with the improvement structures, one habitat value was calculated for each case, by adding all the WUAs weighed by the frequency of their corresponding discharges. This value was called “Frequency Weighted Habitat” (FWH). In this way, the most frequent flows have more influence in the final result. To carry out this analysis, we used daily flow data supplied by Spanish Water Authority (“Confederación Hidrográfica del Norte”). These data, ranging from October 1965 to September 1997, were recorded at the gauging station no. 215, located in the town of Puente Viesgo, 750 m downstream the study reach. For the calculation of fry and parr FWH, the whole data set were employed, while adult FWH was obtained considering only daily discharges from March to December, when adult salmons are in the river for reproduction. Frequency Weighted Habitat signifies the real habitat improvement under natural regime.

5.3. Results

Discharge at the time of survey was 1.05 m3/s in the studied reach. Water heights were 67.39 m upstream and 66.92 m downstream. These water surface heights are relative elevations that have no absolute physical meaning. The adjusted values for “a” and “b” coefficients in each topographic case, from the expression q = a⋅db are shown in Table 5.2. These coefficients were calculated from the boundary conditions at the initial calibration of the model, and are used to carry out the hydraulic simulation.

Table 5.2. Coefficients from the expression q = a⋅db, after calibration with discharge and water height data. Case Coefficient “a” Coefficient “b” Current conditions 1 2.096 With three triangular deflectors 1 2.243 With a low dam 1 2.097 Geomorphological changes due to deflectors 1 2.243 Geomorphological changes due to low dam 1 2.103

Figure 5.6 represents the ground plan of the reach selected to develop the study, as it was taken in the fieldwork. The shore line at the time of survey is also shown.

Microhabitat simulation of instream habitat enhancement options

Figure 5.6. Topography of the studied reach, showing the shore line at the time of survey (white line).

The software River2D divides the reach in a series of cells. After a simulation, it provides several outputs for each cell, such as depth, water surface elevation and velocity, Froude number and suitability indexes for the indicated development stage. Figure 5.7 is an example of these outputs where we can see depth (in grey scale) and water velocity (as vectors), for an instream flow of 1.05 m3/s (the one measured in the fieldwork). Figures 5.8–5.10 show the combined suitability index for the considered development stages: adult, parr and fry. Combined suitability index has been calculated as the geometric mean of depth, velocity and substrate suitability indexes, in order to be comparable with other simulations, even if they have used a different number of habitat variables.

Figure 5.7. Depth and water velocity in the studied reach for the discharge measured in the fieldwork.

Microhabitat simulation of instream habitat enhancement options

Figure 5.8. Combined suitability index for Atlantic salmon adult, for the discharge measured at the time of survey.

Figure 5.9. Combined suitability index for Atlantic salmon parr, for the discharge measured at the time of survey.

Microhabitat simulation of instream habitat enhancement options

Figure 5.10. Combined suitability index for Atlantic salmon fry, for the discharge measured at the time of survey.

Figure 5.11 shows how Weighted Useable Area changes depending on the instream flow variations, in natural conditions, for each Atlantic salmon development stage. Adult habitat maximum is around 30 m3/s, which only occurs during high flows. Though, parr and fry habitat reaches its maximum earlier, around 12 m3/s.

1600 1400

1200 Adult 1000 Parr )

2 Fry 800 600 WUA (m WUA 400 200 0 0 10203040 Stream flow (m3/s)

Figure 5.11. Relationship between WUA and stream flow under unmodified conditions.

Microhabitat simulation of instream habitat enhancement options

Simulation with three alternate triangular deflectors

A series of three alternate triangular deflectors was placed over the previous unmodified riverbed (Fig. 5.12). This new topography was introduced into the software River2D to carry out a new hydraulic simulation.

Figure 5.12. Location of the three triangular deflectors (drawn in white) in the studied reach.

The simulation of this new surface, gave another Flow-WUA curves. Subtracting these curves from the ones achieved under unmodified conditions, the chart illustrated in Figure 5.13 was obtained, in which we can see the habitat improvement reached for each development stage within the flow range of 0–40 m3/s. Positive values represent a habitat gain with the new structures. 50

40 Adult 30 Parr 20 Fry 10 0 -10 -20 WUA Defl - Natural WUA -30 -40 0 10203040 Stream flow (m3/s)

Figure 5.13. Habitat improvement reached for each flow value, when introducing a series of three alternate triangular deflectors in the riverbed.

Microhabitat simulation of instream habitat enhancement options

A new riverbed topography was simulated considering geomorphological changes due to deflectors, and the chart in Figure 5.14 was obtained. It shows, similarly as Figure 5.13, the difference with WUA in natural conditions, so it tells us about the habitat improvement for each discharge and development stage.

60 50 Adult 40 Parr 30 Fry 20 10 0 -10 -20 -30

WUA after Defl --40 Natural WUA 0 10203040 Stream flow (m3/s)

Figure 5.14. Habitat improvement reached for each flow value, considering the geomorphological adjustments caused by the presence of the series of three alternate triangular deflectors.

Adult habitat experiences an improvement within the flow range of 6–20 m3/s, when introducing deflectors in the riverbed (Fig. 5.13). For higher discharges it decreases. When the topography changes by geomorphological adjustments (Fig. 5.14) this habitat gain is higher, and also the improvement range increases to 4–26 m3/s.

Parr and fry curves behave similarly, except that parr habitat increases more between 10 and 20 m3/s than fry habitat. In the simulation with deflectors, both of them show an increment between 2 and 32 m3/s, but it is only important until 18 m3/s. For larger discharges they move around 0 m2 of improvement. After topographic changes due to the new erosion and sedimentation, parr and fry habitat improves even more, but within the same interval.

With daily flow data from a 30 years period, Frequency Weighted Habitat (FWH) was calculated as it is explained in the methods. The FWH represents the real habitat improvement under natural flow regime, for each development stage in each case: unmodified conditions, alternate deflectors, low dam and after geomorphological adjustments due to the presence of each habitat improvement structure.

Figure 5.15 shows the evolution of the Frequency Weighted Habitat from natural conditions to the introduction of deflectors, and finally to the geomorphological

Microhabitat simulation of instream habitat enhancement options adjustments case. Units in Y-axis are the percentage of improvement regarding to natural conditions habitat.

1.6 1.4 Adult 1.2 Parr 1 Fry 0.8 0.6

% improvement 0.4 0.2 0 Natural With deflectors After gm. adjustments

Figure 5.15. Habitat improvement as a percentage of WUA in control conditions, in deflectors case.

Chart in Figure 5.15 shows how habitat improvement fluctuates for each development stage and for both cases. There is no significant increase, because almost every point is between 1 and 1.4% of improvement, which is really small.

Simulation with a low dam

The second improvement measure considered in this work was the introduction of a low dam in a narrow zone of the reach. The dam is low enough to allow water to pass over it, and it has also a slot to facilitate fish pass when little flows occur. As Figure 5.16 shows, we introduced the dam shape into the bed topography.

Figure 5.16. Location of the low dam (drawn in white) in the studied reach.

Microhabitat simulation of instream habitat enhancement options

New Flow-WUA curves were obtained after simulation of the new surface with the dam. By comparing them with the ones obtained for the natural reach, we achieved the habitat improvement chart in Figure 5.17.

35 30 Adult 25 Parr 20 Fry 15 10 5 0 -5 -10

WUA Dam - Natural-15 WUA -20 0 10203040 Stream flow (m3/s)

Figure 5.17. Habitat improvement reached for each flow value, when introducing a low dam in the riverbed.

The supposed dam also produces changes in the geomorphology of the river. Hence, the bed topography was modified according to these adjustments and a simulation was carried out again. The results of this last simulation, after its comparison with control curves, are shown in Figure 5.18.

20 Adult Parr 15 Fry 10

-5

WUA after Dam - Natural WUA-10 0 10203040 Stream flow (m3/s)

Figure 5.18. Habitat improvement reached for each flow value, considering the geomorphological adjustments caused by the presence of the low dam.

Microhabitat simulation of instream habitat enhancement options

The introduction of a low dam in the river causes an increment in the adult habitat within nearly the whole simulated range of discharges. When considering the geomorphological adjustments (Fig. 5.18), the maximum increase (at 10 m3/s), is smaller than when just the low dam is placed (Fig. 5.17), but for higher flows the second case increases more adult habitat, having another maximum at 25 m3/s.

Like in deflectors case, parr and fry curves have a similar behaviour. They contain at least two relative maximums: one around 5 m3/s and the other at 30 m3/s. They experience a habitat improvement only for small discharges, except parr habitat in geomorphological adjustments case (Fig. 5.18) but with a very little enhancement for high flows.

After these simulations with the low dam, the same flow frequency study as in deflectors case was done, to have a quantitative evaluation of this improvement measure in river Pas. These results are showed in Figure 5.19, where it is possible to observe how the habitat would improve, under natural regime, if the considered low dam was introduced.

1.6 1.4 Adult 1.2 Parr 1 Fry 0.8 0.6

% improvement 0.4 0.2 0 Natural With dam After gm. adjustments

Figure 5.19. Habitat improvement as a percentage of WUA in natural conditions, in low dam case.

As in deflectors case, Figure 5.19 shows non-significant habitat increases that are even smaller (less than 1%) than the ones obtained when introducing the series of deflectors.

Instream habitat enhancement measures assessment

In control conditions, the adult habitat increases with discharge until it reaches a maximum around 30 m3/s, then it starts to decrease. Parr and fry habitat curves behave similarly, but they reach their maximum earlier, around 12 m3/s (Fig. 5.11). This is logical because depth and water velocity increase with flow, and adults prefer deeper areas and tolerate better high water velocities, as shown in the preference curves (Figs. 5.3 and 5.4).

Microhabitat simulation of instream habitat enhancement options

A series of deflectors in the stream improve adult habitat between 6 and 20 m3/s, but not for greater discharges. After some time, when the riverbed has been adjusted due to the new erosion and sedimentation areas caused by the presence of these deflectors, the habitat gain is larger, and also the improvement range increases to 4–26 m3/s. Parr and fry experience an increment of habitat within the range 2–32 m3/s, but it is only important until 18 m3/s. After geomorphological adjustments, their habitat increases a little more. Overall, the real habitat improvement under natural regime is really small and not significant at all. It fluctuates between 1 and 1.4% of improvement regarding to control conditions.

The introduction of a low dam in a narrow zone of the studied reach increases adult habitat within nearly the whole simulated range of discharges. After geomorphological changes the habitat increment is greater for higher flows. Parr and fry habitat improves nearly only for small discharges. The real habitat increase under natural regime is lesser when considering the new erosion and sedimentation areas generated by the dam. This increment is not significant (less than 1%), as it happened in deflectors case.

5.4. Discussion

Instream Flow Incremental Methodology (IFIM) was originally developed as a specific tool to assess impacts caused by flow alteration, changes in channel morphology and in water quality (García de Jalón et al. 1993). The strength of IFIM in restoration programs planning lies on its capability for hydraulic simulation, which allows to evaluate the new available habitat, by comparing it with the original conditions or with other restoration scenarios.

In this work, the two-dimensional hydraulic model has described the detailed local distribution of depths and velocities, and the physical habitat complexity for several flows under different topographic scenarios. Since the analysis focus on the consequences of introducing some particular structures in the riverbed, which will change flow direction and create turbulence, the diversity of depths and water velocities and its detailed distribution acquires a great importance.

Two-dimensional hydraulic models have been employed by other authors to study fish habitat in several conditions. Vehanen et al. (2003) used a 2D model to assess the effectiveness of habitat enhancement measures for grayling (Thymallus thymallus) in a channeled river reach, which was restored by building small islands and reefs as well as cobble and boulder structures. A 2D model has been also employed by Leclerc et al. (1995) to study the habitat of juvenile Atlantic salmon of the Moisie River (Quebec) where a water diversion was planned. Ghanem et al. (1996) used one- and two- dimensional approaches to simulate the flow of water in a real fish habitat reach. They compared the computed velocity results with field velocity measurements. Results of the two-dimensional approach appeared to be significantly better than the one- dimensional ones.

Habitat simulation is employed in this work to estimate the effectiveness of different instream habitat improvement options (the inclusion in the riverbed of current deflectors and a low dam), prior to their construction. The simulation results allow to establish

Microhabitat simulation of instream habitat enhancement options whether a concrete action will significantly enhance fish habitat, quantifying this enhancement and thus, whether it will be worth to be executed.

By comparing habitat evaluations in the control conditions with those obtained under the improvement conditions, their effectiveness can be assessed. The habitat simulation results show that with both improvement measures, under a natural flow regime the mean annual habitat increases around 1% of the WUA in relation to the control conditions, which is not a significant improvement. We obtained the same small WUA increase, when changing the bed topography, considering geomorphological adjustments due to the new erosion and sedimentation areas caused by the presence of these structures.

The very little habitat improvement attained (in some cases even a reduction) under these instream habitat enhancement options, shows that in this particular river reach it is not useful to implement the analysed measures. Deflectors and low dams contribute to water velocities and depths heterogeneity and therefore, to microhabitat diversity. However, this increase in habitat heterogeneity has not supposed a significant increase on salmon required habitat. Of course these results should not be generalized, as physical habitat improvement has been successfully applied in other cases. Hale (1969) has shown in two Minnesota streams that the carrying capacity for trout populations was increased after habitat restoration by means of similar enhancement measures.

The introduction of habitat improvement structures in the riverbed gives its best results in channeled streams, which are very homogeneous, because they increase the hydraulic heterogeneity. For this reason, we believe that the scarce habitat improvement is due to the characteristics of the studied reach, which is already quite heterogeneous. Atlantic salmon habitat does not increase in this reach with these habitat improvement options. Therefore, these types of habitat enhancement measures are not recommended in this case. Managers should look for the actual bottlenecks in order to minimize their effects.

Often, physical habitat improvement measures are undertaken without a previous diagnosis of the problem, which leads to a failure in the final objective, the enhancement of the target fish community. These actions are best carried out through a formally planned project, with multiple objectives (Gardiner 1991) and directed by multidisciplinary teams using a bioengineering assessment (Osborn & Anderson 1986). Firstly, an evaluation of fish populations and their habitat should be done, taking into account the five main components of fish habitat, namely: spawning areas, food- production areas, refuge zones, flow regimes and water quality (García de Jalón 1995). With this knowledge it is now possible to realize which are the habitat problems and to find out if there are any population bottleneck. Only after this stage, we can consider the possibility of carrying out any physical habitat improvement measure, but before its implementation it is advisable to use habitat simulation as a tool for predicting and quantifying the final result of our actions. If finally some habitat improvement structure is placed, it will be essential to monitor its effectiveness and maintenance. Reeves et al. (1991) have suggested that the monitoring program must focus both on quantitative evaluations of habitat change and on fish population changes.

6. MESOHABSIM APPLICATION FOR RIVER RESTORATION

This chapter has been published in a peer-reviewed international scientific journal:

Gortázar, J., Parasiewicz, P., Alonso-González, C. & García de Jalón, D. (2011). Physical habitat assessment in the Tajuña river (Spain) by means of the MesoHABSIM approach. Limnetica, 30, 379–392.

MesoHABSIM application for river restoration

6.1. Introduction

There are several tools for the assessment of physical habitat in rivers on a microhabitat scale, such as PHABSIM (Bovee 1982), RHYHABSIM (Jowett 1989), and EVHA (Ginot 1995). Habitat studies in a river channel can be applied on different scales of analysis, such as the microhabitat or mesohabitat scales. For instance, microhabitat scale models within the IFIM framework have been broadly used in Spain, primarily for the establishment of ecological flow regimes.

The approach used in this work, MesoHABSIM, involves the use of the mesohabitat scale in data acquisition and analysis, and this defines its characteristics and applications. MesoHABSIM is an approach for modelling physical habitat in rivers. It allows a user to quantify the available habitat for selected fauna under specific environmental circumstances, and it can be used to simulate diverse scenarios, such as river alterations or restoration measures (Parasiewicz et al. 2009). The rationale of MesoHABSIM is the recognition that fauna reacts to the environment on different scales related to the size and mobility of the species as well as to the time of use. Therefore, the effort is focused on the habitat characterisation on the meso-scale level. Meso-scale units can be defined as areas where an animal can be observed for a significant portion of its diurnal routine, and it roughly corresponds with the concept of “functional habitat” (Kemp et al. 1999). Observation on the meso-scale can be expected to provide meaningful clues about an animal’s selection of living conditions (Hardy & Addley 2001).

This paper describes the first application in Spain of the MesoHABSIM approach. It was an eminently practical application with the following objectives: to evaluate the current habitat availability for the brown trout in the Tajuña river and to quantify the habitat improvement that would be obtained if a specific restoration action was implemented.

6.2. Methods

The work presented here applies the underlying concepts of the MesoHABSIM approach and its fundamental methods, but it is a simplification of the standard MesoHABSIM process, primarily due to budget constraints. The main modifications from the usual process are the following: (1) although MesoHABSIM usually considers habitat use within a range of flow values, in this work, the habitat was analysed under a single flow magnitude; (2) our study focuses on a single species, the brown trout (Salmo trutta L.), while MesoHABSIM permits consideration of the entire fish community; and (3) the biological models that define the kind of habitat used by the fish are normally generated by sampling fish with “electrogrids” (Bain et al. 1985) in several mesohabitats and processing data with logistic regression analysis, but in this work, we have built literature-based models and calibrated them with the available electrofishing data.

MesoHABSIM application for river restoration

Study site

The Tajuña river is located in the province of Guadalajara, Spain, and it flows westward into the Jarama river, which belongs to the Tagus basin. The study site was a 116 km long segment of the Tajuña river between its source and the confluence with its tributary, the San Andrés river. Brown trout (Salmo trutta L.) is the most abundant species inhabiting the study site. Other autochthonous species in the fish community are barbel (Barbus bocagei Steindachner), Iberian nase (Pseudochondrostoma polylepis Steindachner), Iberian chub (Squalius pyrenaicus Günther), “bermejuela” (Achondrostoma arcasii Steindachner), “calandino” (Iberocypris alburnoides Steindachner) and Iberian loach (Cobitis paludica de Buen). The exotic species present are gudgeon (Gobio lozanoi Doadrio & Madeira) and rainbow trout (Oncorhynchus mykiss Walbaum).

The most obvious alterations of the Tajuña river are caused by agriculture on the margins of the river course. This land use is responsible for several stressors that configure the current geomorphology of the river (Palmer et al. 2010). The main alterations are the rectification of several reaches that reduce sinuosity, the incision and entrenchment of the riverbed, which makes the channel deep and homogeneous, and the degradation of the riparian vegetation, along with an important flow depletion and regulation to irrigate the adjacent crops.

Segments 2, 7, 8 and 12 (see below) do not suffer from these alterations, and their physical habitat is little altered or unaltered because they flow through narrow valleys with poor accessibility where it has not been possible to develop agricultural fields. These segments show a diversity of HMU types, with notable numbers of riffles and rapids. Cascades, backwaters and side arms are also present, thus providing a diversity of microhabitats. Therefore, restoration was not a priority in these segments.

MesoHABSIM application

A detailed description of the MesoHABSIM approach is provided in Parasiewicz (2007a) and Parasiewicz et al. (2009). It follows the typical structure of habitat models described by Parasiewicz & Dunbar (2001) and is an aggregation of three models: (1) a hydromorphologic model that describes the spatial mosaic of fish-relevant physical features, (2) a biological model describing habitat use by animals, and (3) a habitat model quantifying the amount of usable habitat.

The study area was stratified in 16 segments (Table 6.1) based on a reconnaissance survey of the whole river and on the interpretation of aerial photographs and focusing on alterations and land use changes along the river. Within each segment, one representative site (1–2 km long) was chosen for the mesohabitat survey, accounting for 16.7 % of the total study area length.

In each site, a mesohabitat survey was performed in June and July, 2009. Every hydromorphologic unit (HMU) within the site was identified, and its outlines were drawn as georeferenced polygons on a Pocket PC, using GIS software and aerial photographs. The physical attributes were estimated for each HMU, and the data were entered into a GIS table associated with the corresponding polygon. The physical attributes considered were the HMU type, the cover types (using three categories:

MesoHABSIM application for river restoration absent, present and abundant), and the proportions of depth, mean column velocity and substrate classes within the unit (for a description of the physical attributes, see Parasiewicz 2007a). Depth, mean column velocity and estimated substrate were measured in seven random locations within each HMU. Measurements for depth and water velocity were taken with a Dipping Bar (Jens 1968). Substrate definitions were based on the substrate classification system according to the Austrian Standard ÖNORM 6232 (1995).

Table 6.1. Characterisation of the 16 segments in the Tajuña river: Length, mean altitude and gradient. Length Altitude Gradient Segment (km) (m a.s.l.) (%) 1 4.77 1164 0.34 2 6.36 1134 0.71 3 8.56 1094 0.41 4 2.19 1073 0.27 5 9.86 1046 0.20 6 5.62 1030 0.21 7 12.01 1001 0.38 8 4.46 897 0.47 9 11.85 887 0.16 10 5.55 863 0.52 11 3.79 839 0.47 12 2.75 824 0.47 13 9.03 798 0.42 14 4.08 775 0.20 15 9.00 752 0.42 16 13.60 718 0.23

During the mesohabitat surveys, the flow related to the catchment area in each site was 0.0011 m3/s⋅km2 (0.1 ft3/s⋅mile2), similar to the usual flow during summer. Therefore, our results refer solely to moderate flow conditions and cannot be applied to spates or extreme droughts.

We generated the biological models by considering the habitat use of three developmental stages of brown trout: fry (0+ age class, fork length < 102.5 mm), juvenile (1+ age class, 102.5 mm < FL < 182.5 mm) and adult (older fish, FL > 182.5 mm). Preliminary biological models were generated based on the literature and were calibrated with electrofishing data obtained in the Tajuña river. To specify the affinity of fish presence with depth, water velocity and substrate, we used suitability curves developed by Bovee (1978, category I curves as defined by Armour et al. 1984, i.e., based on literature sources or professional opinion), Raleigh et al. (1986, category I) and Heggenes (1990a, category III or preference curves). To define the brown trout preferences for HMU and cover types, the review on brown trout habitat requirements by Armstrong et al. (2003) was consulted. When the literature provided different values for distinct parts of the year or flow magnitudes, we used those that referred to either summer or moderate flow conditions because our study focused on habitat availability with moderate flow, which we assume occurs during the summer and, some years, even during a part of the spring and fall. The preliminary model consisted of certain HMU

MesoHABSIM application for river restoration types, intervals of depth and velocity, and features of cover and substrate that are associated with a “suitable” habitat (if 3 of them occur) or with an “optimal” habitat (if 5 of them occur); the model is not a curve or a continuous variable but intervals or features to be considered in the habitat evaluation.

Once the preliminary biological models were generated, they were calibrated with quantitative electrofishing data to maximise the model’s ability to explain the observed trout densities. Electrofishing data were obtained during July 2009, in a period of flow similar to that of the mesohabitat survey. Fish were sampled with pulsed direct current in twelve river reaches, each ca. 100 m long, located within the mapped sites. Trout density was estimated with the three-pass removal method, following Carle & Strub (1978). To calibrate the model for each developmental stage, we performed a linear regression analysis in the sites with available electrofishing data, relating the following two variables: brown trout density (individuals/m2) and proportion of effective habitat within the electrofished area related to the mapped surface. Effective habitat is an aggregation of suitable and optimal habitats with different weights to assure a high contribution of the “optimal” habitat: Effective habitat = Suitable habitat + 1.5 Optimal habitat. Model calibration consisted of an iterative process: for each iteration, one class of one variable (a single depth, a velocity or substrate class, or a single HMU or cover type) was included in the model or excluded from it, and the linear regression analysis between trout density and effective habitat was calculated again. After each change, if the regression analysis reflected a better adjustment, the change was included in the model. Otherwise, the change was rejected.

The combination of the existing habitat features (hydromorphologic model) and the habitat used by the fish (biological model) provided the habitat model, which classifies the suitability of each HMU into three categories: not suitable, suitable and optimal (see Parasiewicz 2007a for more detail).

Physical habitat restoration

To propose specific restoration measures to improve habitat availability, it is necessary to identify which habitat attributes should be increased or decreased. To make these ecologically based decisions, it was necessary to identify the current habitat deficits for the three life stages of brown trout and to determine at which life stage habitat is more limited.

For the identification of habitat deficits, the current physical habitat characteristics (Table 6.2) were compared with the needs of brown trout as detailed in the biological models. Habitat deficits were identified by visual inspection of Table 6.2.

To identify the life stage with the most limited habitat, we compared the current proportions of available habitat for each developmental stage and the habitat proportions needed by a brown trout population with a balanced age structure. The population structure observed in the segments without physical habitat alterations (sites 2, 7, 8 and 12) was considered to be the balanced or “ideal” population structure. Electrofishing data from these sites were pooled, and the linear regression between age class density and age was computed. The slope of this regression (mortality) was used to derive the habitat distribution of a balanced population structure after a correction for

MesoHABSIM application for river restoration the relative amount of habitat needed by each life stage as given by Bovee (1982): adult habitat area/fry habitat area = 1/0.3; adult habitat area/juvenile habitat area = 1/0.8.

Table 6.2. Physical habitat characteristics in the current summer conditions. For HMU and cover types, the proportion of HMUs with the attribute is given (cover types are not mutually exclusive). For depth, velocity and substrate, the proportion of each class in the study area is given. Superscripts indicate suitable attributes for adult (A), juvenile (J) or fry (F), based on the biological models. Depth (cm) Prop. Velocity (cm/s) Prop. Substrate Prop. < 25 0.29 < 15 A, J 0.37 Pelal A 0.27 25–50 J, F 0.30 15–30 A, J, F 0.24 Akal F 0.04 50–75 A, J, F 0.17 30–45 A, J, F 0.19 MicroLithal F 0.20 75–100 A, J 0.08 45–60 J, F 0.10 MesoLithal A, J 0.14 100–125 A 0.05 60–75 0.05 MacroLithal F 0.06 > 125 A 0.10 75–90 0.03 MegaLithal A, J, F 0.06 90–105 0.01 Phytal 0.13 > 105 0.02 Psammal 0.08 Xylal 0.01 Sapropel 0.01 Debris 0.00 Detritus 0.00 Gigalithal 0.00 HMU type Prop. Cover type Prop. Rapids A, J, F 0.05 Boulders A, F 0.33 Riffle A, J, F 0.08 Undercut A, J, F 0.33 Pool A, J 0.18 Shallow margin F 0.41 Side arm F 0.00 Submerged Veg. A 0.50 Backwater F 0.00 RipRap 0.04 Cascade 0.00 Overhanging Veg. 0.71 Ruffle 0.18 Canopy Cover 0.76 Run 0.20 Woody Debris 0.40 Glide 0.29 Low Gradient 0.89 Plungepool 0.00

We have selected restoration measures with the purpose of increasing the availability of brown trout habitat. Using the stream classification by Rosgen (1994), all the segments corresponded to class G (Gc stream type, as the river slope is lower than 2 %). The G stream type is characterised by an entrenched channel, low width/depth ratio and moderate sinuosity. Based on this stream classification, we proposed to implement restoration measures that would convert the current G stream type to a C type, as proposed by Rosgen (1997) for the restoration of incised rivers. The C stream type is characterised by a low gradient (< 2 %), a meandering channel, low entrenchment, and riffle-pool morphology.

To implement this restoration, land use should be distanced from the river, thus opening a buffer that would allow the river to develop its natural processes (González del Tánago & García de Jalón 2007). The streambank walls should be excavated, thereby widening the river channel, and the material should be used to raise the bed elevation.

MesoHABSIM application for river restoration

This would reduce the bank height and erosion rate by decreasing the stress near the banks (Rosgen 1996), thus reducing the sediment input into the channel. In the rectified reaches, a more sinuous channel should be constructed instead of the current straight channel. In general terms, the river channel would become wider and shallower. Afterwards, the river banks should be reforested with indigenous riparian species, and a flow regime as close to natural as possible should be implemented.

A simulation of this restoration action in the Tajuña river was done by means of the MesoHABSIM approach and using the software Sim-Stream 7.1. The restoration was only simulated in altered sites (excluding the unimpaired segments 2, 7, 8 and 12). The simulation has been conducted by the generation of new hydromorphologic models of the restored river segments. The new models were created by changing some attributes of the current models in the way that they would be expected to change if the restoration was implemented, based on the literature and expert opinions, and also considering the relative presence of the attributes in the unimpaired sites. The changes in the hydromorphologic models for the restored scenario were the following (Grant et al. 1990; Rosgen 1994, 1996, 1997; Palmer et al. 2010). First, the area occupied by runs and glides, which are currently the most abundant HMU types, was reduced and partly replaced by riffles and some rapids, that is, 33 % of the surface occupied by runs and 29 % of the glide area were substituted by riffles (85 % of the changed area) and rapids (15 %). Also, the pelal and phytal substrates were reduced (15 % of pelal and 35 % of phytal points) and were replaced by the substrate types that were beneath the fines and plants in the same proportion that they were observed in the unaltered sites: akal (3 %), microlithal (31 %), mesolithal (38 %) and macrolithal (28 %). Additionally, all the riprap was removed, and submerged vegetation was reduced (51 % of HMU surface with this attribute). In the reaches with degraded riparian vegetation, canopy shading (36 % of the mapped area) and overhanging vegetation (25 %) were increased. Shallow margins were also increased (40 %), as parts of the river would become wider and shallower.

The depth would decrease in general due to the new channel width, but the implementation of a natural flow regime could potentially compensate for that by providing higher flow during summer, as currently there is a substantial water withdrawal in the summer for irrigation. Because only one type of flow condition has been sampled so far, the depths were not changed in the model. After the application of the attribute changes described above, the model was run again.

To evaluate whether the proposed restoration would provide a significant increase in the amount of available brown trout habitat and which would be the most appropriate segments for restoration, the amount of effective habitat was compared between the current summer conditions and the restored scenario.

6.3. Results

The electrofishing data used for model calibration provided a brown trout total catch ranging from 0 to 100 individuals and trout density varying from 0 to 0.289 individuals/m2. The calibration of the biological models (Table 6.3) yielded significant linear regressions between trout density and the proportion of effective habitat (Fig. 6.1). The habitat availability obtained by the application of the biological models

MesoHABSIM application for river restoration explained 40 % of the brown trout density for both adults and fry (p < 0.05). Juvenile habitat availability explained 52 % of the juvenile trout density (p < 0.01).

Table 6.3. Biological models generated for brown trout in the Tajuña river. Attributes used by each developmental stage for the variables HMU type, cover type, depth, water velocity and substrate. If the five conditions were satisfied, the HMU was evaluated as optimal habitat. If three conditions (four for the adult) were satisfied, the HMU was evaluated as suitable habitat. Variable Adult Juvenile Fry HMU type Rapids Rapids Rapids Riffle Riffle Riffle Pool Pool Side arm Backwater Cover type Boulders Undercut Boulders Submerged veg. Shallow margin Undercut Undercut Depth > 50 cm 25–100 cm 25–75 cm Water velocity 0–45 cm/s 0–60 cm/s 15–60 cm/s Substrate Pelal Mesolithal Akal Mesolithal Megalithal Microlithal Megalithal Macrolithal Megalithal

R2 = 0.40 0.08 R2 = 0.52 Adult p < 0.05 0.2 Juvenile p < 0.01 0.06 0.15

) 0.04

2 0.1

0.02 0.05

0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 -0.05 R2 = 0.40 0.12 Fry p < 0.05 0.1 0.08

Trout density (individuals/m density Trout 0.06 0.04 0.02 0 -0.02 0 0.2 0.4 0.6 0.8 1 Proportion of available habitat

Figure 6.1. Validation of the biological models for adult, juvenile and fry brown trout. Linear regression analysis between density (individuals/m2) and proportion of effective habitat related to the mapped surface within the electrofished area (n = 12).

MesoHABSIM application for river restoration

The application of the biological models in the mapped sites allowed us to quantify the amount of optimal and suitable habitat for each developmental stage at every site (Fig. 6.2). Optimal habitat was only relevant for adults, while fry and juvenile habitat availability was mainly made up of HMUs that were classified as suitable.

1 Optimal habitat Adult Suitable habitat 0.8

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0 12345678910111213141516

1 Juvenile 0.8

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Proportion of habitatarea 0 12345678910111213141516

1 Fry 0.8

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Figure 6.2. Proportion of suitable (open bars) and optimal (solid bars) habitat area related to the total mapped area in each site for adult, juvenile and fry brown trout.

The visual inspection of Table 6.2 allowed us to identify the main habitat deficits in the current conditions. The most abundant HMU types (glide, run and ruffle) are not adequate for any life stage, while there is a need for more rapids and riffles and for shallow-slow HMU types such as backwaters and side arms. Regarding cover types, Table 6.2 shows that the increase of undercut banks, boulders and shallow margins would improve habitat suitability. There is an excess of the pelal (which is acceptable for adults, but not for juveniles and fry) and phytal substrates, but there is little akal,

MesoHABSIM application for river restoration macrolithal and megalithal available. The augmentation of these attributes would lead to an increase in habitat availability for brown trout.

Figure 6.3 shows that fry habitat seemed to be the limiting factor for the development of a balanced population structure in the Tajuña river, as a balanced or “ideal” population requires more fry habitat than is currently available. Therefore, for the design of the restoration measures, we focused on the habitat needs of brown trout fry.

0.5 Current available habitat Balanced population structure 0.4

0.3

0.2

0.1 Proportion of habitat area (%) area ofhabitat Proportion 0 Adult Juvenile Fry Figure 6.3. Comparison for the whole study area between the proportion of available habitat for each developmental stage in the current conditions (open bars) and the habitat needed to sustain a balanced population structure (solid bars).

The results of habitat availability in the restored scenario were compared with the current conditions. The effective habitat (Fig. 6.4) increased after restoration in almost all instances, except for the adult habitat in some cases (sites 1, 5, 6 and 11). However, the increases were substantial only at sites 1, 4 and 16. At site 1, the large increase in juvenile and especially fry habitat compensated for the slight reduction in adult habitat. Site 4 also showed a great increase in habitat availability, which was dramatic for fry habitat. At site 16, the largest increase was in juvenile habitat availability.

6.4. Discussion

We showed how physical habitat can be assessed using the MesoHABSIM approach, and we have developed a model of the river that allows us to quantify the available habitat for brown trout when using a single flow value. After this process, the habitat model can be used for a variety of purposes, including the identification of fish habitat deficits, river restoration planning or environmental impact assessment. As a demonstration of the possibilities of MesoHABSIM, we have evaluated how available trout habitat would change if a specific restoration action was implemented in the Tajuña river.

MesoHABSIM application for river restoration

1 Adult 0.8

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0 134569101113141516

1 Juvenile 0.8

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0 134569101113141516 Proportion of effective habitat area ofProportion effective habitat 1 Current conditions Fry Restored 0.8

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0 134569101113141516

Site

Figure 6.4. Effective habitat for each developmental stage in the current conditions (open bars) and after restoration (solid bars). Proportion of effective habitat area related to the total mapped area in each site.

The quantification of effective habitat before and after restoration (Fig. 6.4) allows natural resources managers to evaluate the consequences of restoration on habitat availability for each life stage of brown trout. This information permits evaluating at which sites the proposed restoration measures are worth implementing, depending on the management objectives. For instance, if the goal is to increase the carrying capacity for brown trout and thus to improve population abundance, then we can recommend restoration of sites 1, 4 and 16 because it has been estimated that they will experience an important physical habitat improvement. The recommendation of restoration at these three sites focuses on the improvement of fry habitat because it is currently the limiting life stage for the development of a balanced brown trout population. Thus, it is plausible that the whole population would increase after restoration even if the adult habitat

MesoHABSIM application for river restoration slightly decreases in some places, as is the case of site 1. Furthermore, the augmentation of fry habitat availability may have a positive effect on population abundance, as it has been shown that high recruitment induces numerically strong year-classes (Lobón- Cerviá 2005, 2009). The three sites recommended for restoration (1, 4 and 16) currently show serious alterations such as channel rectification, entrenchment and the degradation of riparian vegetation, so our field observations are congruent with the simulation results.

Regarding the approach used in this work, the use of the mesohabitat scale may involve a loss of accuracy because the meso-scale uses larger scale variables and classes for some variables (e.g., depth and water velocity) instead of the continuous variables used by other models. However, in broad-scale studies, the presumed accuracy loss is overcome by the use of habitat units that are more consistent with the scale of the study (Parasiewicz 2007a). If the mesohabitat scale sacrifices some detail, however, it can reveal larger spatial and temporal ecological patterns that represent system properties (Jewitt et al. 2001). Furthermore, MesoHABSIM is able to quickly collect detailed information about physical conditions from long river reaches (Parasiewicz 2007a), and thus, it can be effectively applied in large-scale projects with reasonable effort. It can easily simulate large scale management actions, allowing for the prediction of effects of measures such as dam removals or extensive channel restoration.

The simulation of a restoration scenario by modifying a physical habitat model, based on existing knowledge of fluvial processes, has been done before by García de Jalón & Gortázar (2007) using the microhabitat model River2D (Steffler 2000; Steffler & Blackburn 2002). In this kind of work, it is important to include enough precision when estimating how habitat attributes will change after restoration to obtain reliable results from the simulation. One good possibility is to create a reference image of the river in natural conditions if the restoration is designed to move the system to its natural state.

In our case, it was not possible to create a reference image of the river in natural conditions because there is a lack of historical information about the river habitat, and the observed geomorphological alterations originated long before the oldest aerial photographs were taken. Moreover, physical habitat in the four unaltered segments cannot be used as reference for the rest of the segments because the two groups of segments do not share the same characteristics in natural conditions. The main difference is in the valley type: the four natural segments are in narrow valleys, and their course is constrained by the hill slopes; the rest of the segments flow through wider valleys, where sinuosity in natural conditions may be greater. For this reason, we have estimated how habitat attributes would change in the restored scenario based on existing knowledge, as explained above.

To obtain reliable results from physical habitat models, another key issue is the capacity of the biological models to reflect actual habitat use by fish in the specific study sites. The biological models attained provide the best possible explanation of trout density by habitat availability in this particular case study. The biological models generated in this work should not be called “preference” models (or “category III” models, Bovee 1986). Rather, they are “use” models because they reflect the fish’s use of a range of habitats that include unpaired and altered habitats.

MesoHABSIM application for river restoration

The range of depth used by fish, according to the Tajuña model, is congruent with the category I curves by Bovee (1978) for the three life stages, as the Tajuña depth range always includes the depths with maximum probability in the Bovee model (Fig. 6.5). The discrepancy is higher with the category I curves given by Raleigh et al. (1986), especially for the adult, as our model uses any depth higher than 50 cm, while Raleigh employs a low probability of use (< 0.4) for depths higher than 120 cm.

1 Fry 1 Bovee 1 Raleigh 0.8 0.8 Tajuña 0.8 0.6 0.6 0.6 Adult 0.4 0.4 Juvenile 0.4

0.2 0.2 0.2

0 0 0 0 60 120 180 240 300 360 0 60 120 180 240 300 360 0 60 120 180 240 300 360 Depth (cm) Probability 1 Fry 1 Juvenile 1 Adult

0.8 0.8 0.8

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0 0 0 0 30 60 90 120 150 0 30 60 90 120 150 0 30 60 90 120 150 Water velocity (cm/s)

Figure 6.5. Comparison for the variables depth and water velocity between the biological model of the Tajuña river (grey horizontal bar) and two category I models, reported by Bovee (1978, thick black line) and Raleigh et al. (1986, thin black line).

The range of water velocity used by adults in our model includes all the values that have a probability higher than 0.5 in the Bovee model. Tajuña velocity use by juveniles includes values up to 60 cm/s, while both the Bovee and Raleigh probability of use is decreasing quickly at this point, reaching approximately 0.3 at 60 cm/s. In our model, fry do not use velocities lower than 15 cm/s. This is congruent with the Raleigh model, which gives low suitability for low velocities, but not with Bovee model, which reports maximum probability for velocity lower than approximately 30 cm/s. The disuse of low water velocity by fry in the Tajuña model may be partly caused by the competition with bigger trout in those microhabitats, displacing fry to other places with higher current and thus higher energetic cost.

In the Tajuña model, fry use the akal (mean diameter between 2–20 mm) and microlithal (20–63 mm) substrate classes, and juveniles and adults use the mesolithal (63–200 mm). This result is congruent with both the Raleigh and Bovee models, which give high probability (> 0.8) for gravel (2–64 mm) use by fry and for cobble/rubble (64–250 mm) use by juveniles and adults. Use of the pelal (silt, clay) class by adults in our model also agrees with the Raleigh model (a probability of 1 for silt). The greatest discrepancies in substrate use between the three models are the following: (1) in our model, the megalithal (> 400 mm) class is used by the three life stages, and fry also use macrolithal (200–400 mm), while both the Raleigh and Bovee models show low

MesoHABSIM application for river restoration probability of use (< 0.2) for boulders (250–4000 mm); and (2) Raleigh and Bovee report gravel use by juveniles and sand use by adults and fry, while our model does not consider use in these instances. These differences can be related to habitat availability in the study sites and to a secondary relevance of substrate in the microhabitat selection because other fish habitat studies in Mediterranean rivers (included Tajuña) have reported that depth and velocity are more important (Martínez-Capel et al. 2009). The use of univariate models is also a limitation that can be overcome within the MesoHABSIM approach using multivariate models of mesohabitat variables developed for brown trout in Mediterranean rivers (Mouton et al. 2011).

Regarding HMU type, our model indicates that rapids and riffles are used by the three developmental stages, which reflects the suitability of these HMU types for brown trout (Mäki-Petäys et al. 1997; Roussel & Bardonnet 1997). This usage may be partly caused by the scarcity of rapids and riffles in the Tajuña river, which are actively used where present, reflecting the importance of instream habitat heterogeneity (Roussel & Bardonnet 2002). Pools are used by juveniles and adults, reflecting the suitability of deep areas for large trout (Modde et al. 1991; Riley & Fausch 1995; Roussel & Bardonnet 1997). In contrast, fry prefer backwaters and side arms, which are shallow and slow-flowing areas where they can avoid competition with larger trout. This observation is congruent with the findings of Mäki-Petäys et al. (2000), who showed that fry prefer low-velocity refuges.

Cover is an important element of instream habitat and has been shown to be related to habitat use by trout (Binns & Eiserman 1979; Heggenes 1988a). In the Tajuña model, the three life stages are favoured by the presence of undercut banks, which seems reasonable because this type of cover provides good shelter for brown trout (García de Jalón & Schmidt 1995). Fry and adults use the boulder cover type, which provides for interstitial space and low water velocity microniches (Heggenes 1988b; Heggenes et al. 1993; Bardonnet & Helland 1994). Fry also use HMUs with shallow margins, reflecting the needs of small trout for shallow and slow-flowing areas.

Therefore, the biological models used in this work are realistic, as trout chose habitat features that are reasonable in light of the existing knowledge. Furthermore, the biological models have been calibrated with abundance data collected in the field, including both altered and unpaired habitats, and thus, they maximise the relationship between habitat availability and brown trout density in this particular case study.

In this work, we have applied the underlying concepts of MesoHABSIM and its fundamental methods. We have shown how this approach can be used to simulate the effects of restoration measures on instream habitat before the restoration is implemented. This information allows water managers to evaluate whether the restoration measures would be effective and to choose the appropriate sites to implement them. The MesoHABSIM approach permits the simulation of diverse changes in physical habitat, such as river alterations or restoration measures, at the catchment scale, and therefore, it is an efficient tool for decision making in the management of rivers and watersheds.

7. GENERAL DISCUSSION

General Discussion

7.1. General Discussion

All along this Thesis, some issues related to salmonid spawning and physical habitat management in southern populations have been addressed. In this section, I summarize these findings and I briefly discuss them in the light of recent findings related to these issues.

Brown trout spawning

Brown trout spawning has been investigated in the river Castril population, which is located in the southern edge of the species’ natural range. By means of redd counts, spawning timing and duration has been identified (chapter 3) and the rate of redd superimposition has been quantified (chapter 4).

Spawning commenced in December and lasted until April, with the maximum activity in February. This finding represents the most belated and protracted natural spawning period reported in the literature up to the date chapter 3 was published. The belated spawning in this southern population can be explained by water temperature, because higher temperatures in southern habitats allow for shorter egg incubation periods (Solomon & Lightfoot 2008). Likewise, altitude may play a similar role by its influence on water temperature and thus on the duration of egg incubation, and therefore it is expected that the lower the altitude the later the spawning date.

A literature review across the brown trout natural range, showed a negative relationship between latitude and spawning mean date (the lower the latitude, the later the spawning time, R2 = 62.8%), while the influence of altitude on spawning date was also negative (as expected) but not significant. This lack of significance might be partly due to the scarcity of altitude data gathered from the literature. Anyway, several works confirm that spawning is earlier at higher altitudes in brown trout (Heggberget et al. 1988; Makhrov et al. 2011; Riedl & Peter 2013) and Atlantic salmon (Webb & McLay 1996). The latitudinal cline of brown trout spawning time is well known and has already been stated (e.g., Klemetsen et al. 2003), but to my knowledge this is the first explicit literature review on this topic. Likewise, it is known that Atlantic salmon spawning time varies with latitude as well, being later in southern than in northern populations (Fleming 1996). In any case, spawning time in wild salmonids is adapted to match an emergence time with optimal environmental conditions for the survival of fry (e.g., Brannon 1987; Heggberget 1988; Fleming 1996).

Literature review also showed a significant negative relationship between latitude and spawning duration: the lower the latitude, the longer the spawning period. However, its explanatory power was limited (R2 = 24.4%), which can be partly due to literature data limitations. Again, altitude did not show any significant effect on spawning duration either. Spawning duration should be controlled by several drivers that are not completely understood, such as flow regime, water temperature, photoperiod, population size or genetic origin, among others. However, this work has shown at least the effect of latitude on spawning duration at a broad scale.

General Discussion

Few investigations have focused on the influence of latitude or altitude on brown trout spawning duration, but some recent works have studied this issue. For instance, Riedl and Peter (2013) have addressed the altitude-spawning duration relationship in several Alpine brown trout populations in Switzerland: Their results were not conclusive, but they reported the longest spawning duration in one of the lowest streams. Regarding other salmonid species, Budy et al. (2012) observed that cutthroat trout (Oncorhynchus clarkii) spawning duration was shorter in high than in low altitude streams, and Webb and McLay (1996) reported that Atlantic salmon spawning was more prolonged at low altitude sites. Interestingly, Heggberget (1988) reported that the spawning period of Atlantic salmon in Norway was more extended in populations which had peak spawning later, and these late-spawning populations inhabit streams with higher water temperatures than early-spawning populations.

There are interesting spawning data from Italian brown trout populations. It should be noted that, according to Kottelat and Freyhof (2007), these Italian trout populations may correspond to the species Salmo cenerinus and Salmo cettii, but their taxonomic status is not yet clear (Gandolfi 2003). In the trout population (tentatively Salmo cenerinus) inhabiting the river Metauro, an Apennine stream with an unpredictable flow regime, the spawning period is quite protracted, lasting about three months, from November to January (Caputo et al. 2010). These authors also suggested that the natural spawning period should be more belated according to the latitude (43º28’N) but it may be altered due to stocking with farmed strains of Atlantic origin. This hypothesis is supported by the observation that trout spawns later (February-March) in other Italian populations from Sibillini Mountains National Park (tentatively Salmo cenerinus as well, Caputo et al. 2003), where a considerable amount of Mediterranean mtDNA haplotypes persisted and genetic introgression is weak (Caputo et al. 2004; Splendiani et al. 2006). In addition, Caputo et al. (2010) also detected some small females spawning in February and March in the river Metauro. Moreover, Gibertini et al. (1990) reported that a native trout population (tentatively Salmo cettii) from the Posta Fibreno lake (Central Apennines, Italy) reproduces from January to March and Duchi (2011) reported that a native Salmo cettii trout population from southeastern Sicily spawns during January and February.

Very important results have been recently published about the spawning period of several brown trout populations in the region of Andalusia (southern Spain). The studied populations show spawning mean dates between January 17th and February 26th (mean: January 31st, Larios-López et al. 2015b), which are in agreement with the latitudinal cline presented in chapter 3. Furthermore, these southern populations show the most protracted spawning periods reported so far: There are females with mature eggs from October to late April, and the spawning extends for 150-170 days (more than five months) in all the populations (Larios-López et al. 2015b). Therefore, the belated and protracted spawning period first detected in the river Castril (chapter 3) is now known to be widespread throughout the Andalusian brown trout populations (Larios- López et al. 2015b). The majority of the studied Andalusian populations live in Sierra Nevada mountain rivers, originating at high elevations (above 3000 m a.s.l.) and having nival (snowmelt) or nivo-pluvial flow regimes. The populations outside Sierra Nevada, in lower elevation streams (rivers Cacín and Castril) and with pluvial (rainfall) and pluvio-nival flow regimes, start the reproduction slightly later than the populations of Sierra Nevada. Larios-López et al. (2015b) argued that the variation in the spawning

General Discussion periods among the Andalusian populations may be caused by differences in water temperature and hydrological regimes.

Furthermore, some mature females have been caught at the beginning of June in one of these Andalusian streams (river Alcázar, Larios-López, personal communication). Delayed spawning of some individuals may be common in brown trout. For instance, (Frank et al. 2012) observed in a fully controlled Belgian brook that, although the spawning occurs from November to January, some trouts (3%) spawned in February and early March. However, these exceptional late spawners may have low reproductive success (Caputo et al. 2010).

The factors that cause the belated and protracted spawning in southern brown trout populations are not yet clear. The time and duration of spawning is to some degree genetically inherited (e.g., Siitonen & Gall 1989; Danzmann et al. 1994; O’Malley et al. 2003; Fleming & Reynolds 2004; Hendry & Day 2005), but phenotypic plasticity driven by environmental factors also plays a role. In this sense, (Makhrov et al. 2011) showed that natural and hatchery-reared Black Sea brown trout (Salmo trutta labrax) displayed different spawning timing, although they all shared the same origin: Hatchery trout matured later and during a longer time than wild trout, and the authors explained this phenomenon by environmental factors, mainly water temperature.

As it has been shown, latitude has an influence on spawning timing. This is logical since water temperature is linked to both latitude and altitude, and spawning is affected by water temperature (e.g., Heggberget 1988; Webb & McLay 1996; Klemetsen et al. 2003; Makhrov et al. 2011; Riedl & Peter 2013; Bennett et al. 2014). Moreover, recent experimental study showed that the typical temperature regime for Alpine river systems was the optimal for maturation of hatchery brown trout males and females, and that increasing the mean temperature by ca. 5 ºC produced a delay in female ovulation along with reduced fertility (Lahnsteiner & Leitner 2013). Further, Warren et al. (2012) observed that elevated summer temperature produces a delay in brook trout (Salvelinus fontinalis) spawning along with a reduction in the number of redds constructed.

Photoperiod is also known to have influence on spawning period (Vlaming 1972; Bon et al. 1999; Bromage et al. 2001; Migaud et al. 2010). Other environmental factors may also drive the spawning timing. For instance, Larios-López et al. (2015b) suggested that the type of flow regime may play a role, depending whether it is more influenced by snowmelt (nival regime) or by rainfall (pluvial regime, for description of flow regime types see e.g., Musy & Higy 2011). Unfer et al. (2011) proposed that the long spawning period in the river Ybbs (Austria), from early November to January, may be an adaptation to the unpredictable and harsh flow regime or it may be the consequence of genetic introgression caused by stocking. In this sense, unusual spawning periods can sometimes be explained by stocking with farmed individuals that have been modified by artificial selection. It is known that fish farming has artificially selected early or late spawning fish in order to distribute the production throughout the year, and this has led to fix these traits in several salmonids, such as rainbow trout (Oncorhynchus mykiss, Scott & Sumpter 1983), Atlantic salmon (Heggberget 1988; Lura & Saegrov 1991; Gross 1998) or brown trout (Forneris 1992). Therefore, in some cases stocking can be the main factor explaining unusual reproductive periods that are not synchronized with local habitat conditions (e.g., Caputo et al. 2010), but this is not the case in most of the

General Discussion

Andalusian trout populations where genetic introgression is very low or absent at all (Almodóvar et al. 2007).

Flow regime unpredictability has also been suggested to have an influence on spawning timing. Southern brown trout populations are subjected to a Mediterranean climate (Gasith & Resh 1999; Tierno de Figueroa et al. 2013), in which stochastic extreme flow events are rather unpredictable: Droughts or peak flows may occur at any time during the spawning season. This interannual unpredictability occurs in Andalusian trout streams (Larios-López et al. 2015b) and particularly in the river Castril (chapter 3, Gortázar et al. 2007). In this sense, spawning during a long period can be an advantage for survival in unpredictable habitats. Stream flow is a major factor controlling brown trout recruitment (e.g., Alonso-González et al. 2004; Lobón-Cerviá 2004, 2007; Lobón- Cerviá & Rincón 2004; Lobón-Cerviá & Mortensen 2005; Unfer et al. 2011), and an extreme flow event, such as a spate, may weaken or destroy the year’s recruitment if it occurs at a critical moment (e.g., Anderson 1983; Jensen & Johnsen 1999; Spina 2001; Cattanéo et al. 2002). In unpredictable habitats therefore, if the reproduction is extended over a long period, it is more likely that at least a part of the recruitment will avoid the damages of catastrophic events and survive. Champigneulle et al. (2003) have suggested that temporal dispersion of spawning is likely to prevent population extinction in brown trout. Bennett et al. (2014) supported that a wide temporal distribution of spawning may be an advantage for cutthroat trout, because it reduces the predation risk by reducing the density of spawners, and it can prevent the complete loss of a year’s recruitment due to stochastic events, such as floods. Nikolsky (1963) also stated, more than fifty years ago, that prolonged spawning ensures the preservation of species under unfavourable abiotic conditions. In this sense, we propose the hypothesis that brown trout spawning duration is partly explained by habitat unpredictability, so that spawning will be longer in unpredictable habitats than in more predictable ones.

Redd superimposition

Redd superimposition is common in salmonids. It has been observed in brown trout populations both resident (Anderson 1983; Champigneulle et al. 1988, 2003; Beard & Carline 1991; Essington et al. 1998; Largiader et al. 2001) and anadromous (Rubin & Glimsäter 1996; Rubin et al. 2004; Youngson et al. 2011). Superimposition has also been documented in other salmonids, such as Atlantic salmon (Fleming 1998; Taggart et al. 2001; Youngson et al. 2011), brook trout (Curry & Noakes 1995; Blanchfield & Ridgway 1997, 2005; Essington et al. 1998), bull trout (Salvelinus confluentus, Baxter & McPhail 1999), kokanee (Oncorhynchus nerka, Morbey & Ydenberg 2003), sockeye salmon (Oncorhynchus nerka, Steen & Quinn 1999; Hendry et al. 2004), chinook salmon (Oncorhynchus tshawytscha, Chapman et al. 1986), coho salmon (Oncorhynchus kisutch, van den Berghe & Gross 1984, 1989), chum salmon (Oncorhynchus keta, McNeil 1969) or pink salmon (Oncorhynchus gorbuscha, McNeil 1964, 1969; Heard 1978; Fukushima et al. 1998). A brief review of some of these works can be found in Greene and Guilbault (2008).

Furthermore, redd superimposition also occurs between different salmonid species. In several cases, introduced rainbow trout has been observed to spawn over redds built by brown trout (Hayes 1987, 1988; Landergren 1999; Scott & Irvine 2000; Rubin et al. 2004) or by native Japanese charrs: Dolly Varden (Salvelinus malma, Taniguchi et al.

General Discussion

2000), white-spotted charr (Salvelinus leucomaenis, Taniguchi et al. 2000) and Sakhalin taimen (Hucho perryi, Nomoto et al. 2010). Other examples are the superimposition of kokanee redds over bull trout redds (Weeber et al. 2010) and the spawning of introduced brown trout over brook trout redds (Sorensen et al. 1995).

The rates of redd superimposition observed in the river Castril are among the highest values reported for resident trout populations (Anderson 1983; Essington et al. 1998; Largiader et al. 2001; Champigneulle et al. 2003), even if compared with superimposition rates in sea trout populations (Rubin et al. 2004; Youngson et al. 2011). In chapter 4 (Gortázar et al. 2012), it has been investigated at the microhabitat scale whether spawning habitat availability can explain the high rates of redd superimposition in Castril or whether other factors are responsible. It was observed that superimposition was not correlated with redd numbers, area of Suitable Spawning Habitat (SSH, defined in chapter 4, Gortázar et al. 2012) or redd density. By means of a simulation-based analysis, it was further observed that superimposition was noticeably higher than expected if redd sites were randomly placed within the available spawning habitat. The sections with high redd density (i.e. high spawner density) had high rates of redd superimposition, as it was expected, because in these cases there is not enough available space for reproduction and superimposition should be frequent. However, in the sections with low redd density (i.e. where spawning habitat is not limiting) redd superimposition was highly variable. The cases where redd density is low but superimposition rate is high (as observed in several of the surveyed sections in Castril) suggest that spawning habitat availability is not the only cause of superimposition. Our results point out that females choose specific sites for redd construction instead of randomly dispersing over the available spawning habitat.

Our findings refute the common assumption that redd superimposition is caused by high density of spawners, i.e. excess of reproductive females and/or scarcity of suitable habitat (Champigneulle et al. 1988, 2003; Beard & Carline 1991; Rubin et al. 2004). Additionally, some studies which have been specifically focused on this topic are congruent with our results. Essington et al. (1998) showed that the presence of an existing redd makes a particular site more attractive for spawning than it would be otherwise. They also provided evidence, by means of a behavioural experiment, that female trout have a preference to spawn on existing redd sites, i.e. to superimpose redds (Essington et al. 1998). Likewise, it has been shown that females tend to spawn close to locations that have already been used (Youngson et al. 2011). Interestingly, Taggart et al. (2001) observed that redd superimposition was common in a Scottish Atlantic salmon population, although it was not correlated to the number of spawners present. To spawn over a previously excavated redd may be advantageous, because the female can reduce the energetic cost of reproduction and increase subsequent fitness. In this sense, previously used redd sites may be easier to excavate and may have less fine sediment (Witzel & MacCrimmon 1983; Chapman 1988; Kondolf et al. 1993; Sternecker & Geist 2010; Franssen et al. 2012). Further, the female may dig up prior eggs of potential competitors enhancing the survival of her own offspring (Essington et al. 1998). In any case, the presence of prior redds is a factor that must be considered when assessing spawning site selection (Youngson et al. 2011).

Several authors have suggested or assumed that redd superimposition may reduce spawning success by damaging or destroying the previously laid eggs, in brown trout populations (Hayes 1987, 1988; Rubin & Glimsäter 1996; Essington et al. 1998; Scott

General Discussion

& Irvine 2000) as well as in other salmonids (McNeil 1964, 1969; Heard 1978; van den Berghe & Gross 1989, 1984; Fukushima et al. 1998; Steen & Quinn 1999; Taniguchi et al. 2000; Morbey & Ydenberg 2003; Hendry et al. 2004; Nomoto et al. 2010).

On the other hand, several works have shown that the negative impact of superimposition over formerly laid eggs is frequently limited. In this sense, Anderson (1983) observed that redd superimposition do not necessarily destroy the contents of pre-existing brown trout redds. Likewise, Youngson et al. (2011) detected that the destruction of previously laid eggs by redd overcutting was very infrequent, and they argued that destructive superimposition must be more common in semelparous salmonid species (Oncorhynchus spp.) spawning at high densities, which explains the aggressive nest defense performed by females of oncorhynchid species (review in Fleming & Reynolds 2004).

The main factor that restricts the potential damage of superimposition over previous redds, is that large females bury their eggs deeper (van den Berghe & Gross 1984; Crisp & Carling 1989; Crisp 1994; Fleming et al. 1997; Fleming 1998; Steen & Quinn 1999; Blanchfield & Ridgway 2005) and spawn early in the season (Elliott 1994; Kitano 1996; Blanchfield 1998; Blanchfield & Ridgway 2005) than small females (reviewed in Fleming & Reynolds 2004). Thus, it is more unlikely that small females can excavate deep enough to dig up the eggs from large and early spawners. In this sense, Blanchfield (1998) explained the delayed breeding of small female brook trout as a tactic for avoiding nest dig-up by large females. Likewise, Blanchfield and Ridgway (2005) reported that large brook trout females spawned earlier and constructed deeper nests, and this fact reduced the potential brood loss by redd superimposition. Therefore, egg burial depth is a key factor controlling egg-to-fry survival when superimposition occurs. For instance, Weeber et al. (2010) observed that bull trout egg-to-fry survival was not affected by later kokanee spawning, because kokanee did not scour the streambed deep enough, whereas Nomoto et al. (2010) suggested that rainbow trout spawning impacts Sakhalin taimen redds because egg burial depth of both species are very similar.

In the river Castril, a negative relationship between redd length and spawning time has been observed, suggesting that large females tend to spawn early in the season (chapter 3, Gortázar et al. 2007), which is congruent with the literature (Elliott 1994; Kitano 1996; Blanchfield 1998; Blanchfield & Ridgway 2005). This could reduce the negative effects of frequent redd superimposition, because the later and shallower redds would suffer less overcutting, whereas the large redds from early spawners would have deeper eggs that are less susceptible to digging-up.

Besides the potential loss of eggs, intense spawning and redd superimposition may also have beneficial effects on spawning habitat: It may improve the streambed conditions for egg incubation, by reducing substrate embeddedness and removing silt and organic matter (Kondolf et al. 1993), thus increasing the porosity and permeability of gravel substrate (McNeil & Ahnell 1964; McNeil 1969; Heard 1991; Rubin et al. 2004).

Finally, it should be noted that the river Castril is a limestone river: Its waters have a considerable concentration of calcium carbonate, which tend to precipitate over the streambed substrate. This chemical process can potentially embed the gravel, making it cohesive and difficult to dig. In this context, it is plausible that redd superimposition provides a significant advantage for female spawners by reducing the energetic cost of

General Discussion reproduction, because digging will be much easier in already used redd sites than in cohesive and highly embedded unused sites. Therefore, we propose the hypothesis that females have a higher preference for superimposing redds in streambeds with cohesive and embedded substrates (such as many limestone streams) than in rivers with loose gravels. Anyway, this hypothesis is yet to be tested.

Physical habitat management

As explained in the General Introduction, physical habitat is determined by the interaction of the structural features of the river channel (gradient, channel size and shape, bank structure, substrate size, etc.) and the hydrological regime, and it is dynamic both in space and in time (Harper et al. 1995; Maddock 1999). Physical habitat management can be an effective tool for the rehabilitation and improvement of salmonid populations (Hendry et al. 2003). Any management action over the physical habitat should be integrated within the broader scale framework of river ecosystems management, which requires a definition of goals and management endpoints (Parasiewicz 2007b).

Many European and Iberian rivers are subjected to significant impacts and pressures (González del Tánago & García de Jalón 2007; Rinaldi et al. 2011), mainly by human use of the river and the surrounding land (Hendry et al. 2003). To counteract these impacts, different techniques for river restoration have been developed, along with a vast scientific background on the functioning of river ecosystems (e.g., Cowx & Welcomme 1998; Clarke et al. 2003; Anderson et al. 2006; Dufour & Piégay 2009; Elosegi et al. 2011; Ibisate et al. 2011; González del Tánago et al. 2012). The key issues in river restoration are the recovery of fluvial connectivity (longitudinal, lateral and vertical), the importance of the hydrological (flow regime, floods) and geomorphological (sediments, channel forms) processes, and the need for sufficient river space (VV. AA. 2011).

River restoration measures include the implementation of ecological flow regimes (Poff et al. 1997; Richter et al. 1997; Anderson et al. 2006; Alonso-González et al. 2008; Milner et al. 2012; Godinho et al. 2014), the enlargement of river space (Malavoi et al. 1998; Piégay et al. 2005; González del Tánago & García de Jalón 2007; Dufour & Piégay 2009; Rinaldi et al. 2011; González del Tánago et al. 2012; Ollero et al. 2014b), the removal of transverse barriers (such as dams) which impede connectivity (Hart et al. 2002; Pizzuto 2002; Stanley & Doyle 2003; Ollero et al. 2014a), the improvement of fish passage at dams (when dam removal is not feasible, Larinier 2002; Porcher & Travade 2002; Morcillo et al. 2008), the recovery of riparian vegetation (González del Tánago & García de Jalón 2007) with autochthonous species (for the Iberian Peninsula see Lara et al. 2004; Garilleti et al. 2012), the creation of vegetated buffer strips (Muscutt et al. 1993; Correll 1997; Tomer et al. 2009), or the use of bioengineering techniques (Schiechtl & Stern 1997; Polster 2002; Sauli et al. 2003) or instream structures to modify physical habitat (García de Jalón 1995; Summers et al. 1996; Antón et al. 2011).

In some instances, not ecological river restoration but habitat enhancement is applied (e.g., Wolter 2010). The difference between enhancement and restoration is that the former seeks increasing the river value not necessarily by recovering its ecological

General Discussion functioning, but rather focused on other facets, such as increasing fish habitat (Kauffman et al. 1997; González del Tánago & García de Jalón 2007). In any case, river restoration and habitat enhancement should take into account natural fluvial processes (Heede & Rinne 1990; Reice et al. 1990; Clarke et al. 2003; Bisson et al. 2009; Ibisate et al. 2011), in order to act along with nature rather than against it, allowing natural processes to restore and improve the stream.

Effective management of physical habitat for salmonids requires an understanding of fish behaviour and ecology and its relationship with the physical characteristics of the stream (García de Jalón 1995). The effectiveness and success of river restoration (or enhancement) actions, strongly depends on prior restoration planning (Parasiewicz et al. 2013b). Ideally, planning should forecast to some degree the results of the designed actions and measures on physical habitat (Lamouroux et al. 2015). In this sense, habitat modeling and simulation is an effective tool for selecting ecologically effective actions and measures and to predict their results (Parasiewicz et al. 2013b).

A large number of habitat models have been developed for the assessment of fish populations and river health. IFIM (Instream flow incremental methodology, Bovee 1982; Stalnaker et al. 1995; Bovee et al. 1998), PHABSIM (Physical habitat simulation system, Milhous & Waddle 2012), RHABSIM (Riverine habitat simulation, Payne 2005), RYHABSIM (River hydraulic and habitat simulation, Jowett 1989), CASiMiR (Computer aided simulation of habitat in regulated streams, Jorde et al. 2000), EVHA (Evaluation of habitat, Ginot 1995), HABSCORE (Milner et al. 1998), MesoHABSIM (Parasiewicz 2001, 2007a) or River Habitat Survey (RHS, Raven et al. 1997, 1998, 2010, applications in Ferreira et al. 2011; Belmar et al. 2013) are among the numerous habitat models available nowadays. The existing modeling frameworks and habitat models have been reviewed by several authors (Maddock 1999; Parasiewicz & Dunbar 2001; Rosenfeld 2003; Conallin et al. 2010; Frank et al. 2011; Armstrong & Nislow 2012).

Since river ecosystems have a hierarchical spatial organization (Frissell et al. 1986; de Boer 1992), habitat assessment can be applied on different spatial scales, and the elements of each level will be governed by processes operating at the levels above (Fernández et al. 2011). Therefore, it is important to be aware of the scale at which we are working and the influence of processes acting at other levels. Different spatial scales can be recognized, such as (ranging from large- to small-scale): watershed, stream system, river reach, mesohabitat, microhabitat (Muhar 1996).

One case of broad scale assessment is the river classification by Rosgen (1994, 1996), which is based on variables such as channel pattern (meandering, braided, etc.), channel slope, entrenchment, width/depth ratio or substrate. The river classification by Orr et al. (2008) is based on variables related to network position (stream order, stream power), topography (slope, presence of floodplain), channel morphology and erosion/deposition processes. Further, the so called River Styles framework (Brierley & Fryirs 2000, 2013; Thomson et al. 2001) rely on the characterization of river attributes from the catchment scale to the reach scale.

MesoHABSIM is a good example of a mesohabitat model, since it is founded on the assessment of relevant habitat features at the mesohabitat scale. At this level, the physical conditions (substrate, hydraulic patterns, cover) around the organism, not at the

General Discussion exact point where it is found, are relevant (Vezza 2010). Therefore, the effort is focused on the identification and characterization of meso-scale units, which can be defined as areas where an animal can be observed for a significant portion of its diurnal routine. Fish meso-scale units typically correspond with hydromorphologic units (HMUs: rapids, pools, glides, backwaters, etc.), thus mesohabitats are defined by the combination of types of HMU with features that provide shelter and create favourable conditions for biota.

IFIM is a modeling framework which can be used along with PHABSIM or other habitat models. It is probably the most widespread methodology at the microhabitat scale. At this level, the exact positions chosen by animals become relevant, and they can be related to small-scale physical features (depth, water velocity, substrate) on a point by point basis, and these data can then be combined to provide values over broader scales. Moreover, some authors have proposed the use of generalized instream habitat models that use simplified hydraulic data (depth-discharge and width-discharge relationships, average particle size, and mean annual discharge), thus reducing field effort and cost. They have shown that these generalized instream habitat models provide results which are similar to those achieved by conventional microhabitat models (Lamouroux & Capra 2002; Lamouroux & Souchon 2002; Lamouroux & Jowett 2005).

Habitat assessment case studies

In the present dissertation, two case studies on physical habitat simulation have been presented. Both habitat assessments have been carried out to foresee the results of certain proposed actions over physical habitat, prior to their implementation. Therefore, these studies allow for estimating the effectiveness of the proposed river restoration or habitat enhancement measures.

In the first case study (chapter 5, García de Jalón & Gortázar 2007), physical habitat was assessed at the microhabitat scale, focusing on the Atlantic salmon population that uses the river Pas (Cantabria, northern Spain) for spawning and rearing. Certain habitat enhancement measures were proposed and their effectiveness was evaluated, under different instream flow conditions, by means of habitat simulation. IFIM was applied along with a two dimensional hydraulic model (River2D, Steffler & Blackburn 2002), and the potential value of stream habitat for different salmon development stages requirements was quantified by means of the Weighted Usable Area (WUA). Habitat simulation was performed for five different scenarios, namely: (1) the unmodified stream reach which represent the control conditions; the original streambed with the inclusion of instream structures for habitat enhancement: (2) three alternate deflectors and (3) a low dam in a narrowing of the reach; and the modified bed topography after the geomorphological adjustments due to the new erosion and sedimentation areas created by the presence of (4) the deflectors and (5) the low dam.

The comparison between the control conditions and the other four scenarios showed that the alternate deflectors would be slightly better than the low dam for habitat improvement. However, the greatest habitat increasing under natural flow regime was below 1.5% of improvement regarding the control conditions. Therefore, habitat simulation results showed that these measures would not provide a significant habitat increase, neither the deflectors nor the low dam.

General Discussion

In summary, the combination of IFIM modeling framework, River2D habitat model and Atlantic salmon preference curves (modified from Heggenes 1990a, 1990b) have allowed for the quantification of habitat suitability in different scenarios, and thereon for the estimation of habitat increasing under each one of the considered habitat improvement measures. Two-dimensional hydraulic modeling has already been employed for the assessment of fish habitat under different conditions (e.g., Leclerc et al. 1995; Ghanem et al. 1996; Crowder & Diplas 2000; Vehanen et al. 2003; Lacey & Millar 2004; Pasternack et al. 2004; Boavida et al. 2012, 2013, 2014).

Habitat simulation has shown that it is not useful to set up deflectors or a low dam in this stream reach. These kind of instream structures can contribute to improve microhabitat diversity by increasing depth and velocity heterogeneity (van Zyll de Jong et al. 1997; Roni et al. 2002; Pretty et al. 2003; Palmer et al. 2010; Whiteway et al. 2010), but in this case the studied reach seems to be already quite heterogeneous and it do not seem to have particular problems on its physical habitat. The introduction of instream structures in the riverbed may give the best results in channeled streams, which are very homogeneous, because they may increase the hydraulic heterogeneity improving habitat suitability for fish (Hendry et al. 2003; Whiteway et al. 2010). Indeed, deflectors and low dams have been successfully implemented in other cases (Armantrout 1991; Frissell & Nawa 1992; Gowan & Fausch 1996; House 1996; Roni et al. 2002), as well as other types of instream structures for physical habitat improvement, such as large woody debris (Bisson et al. 1987; Roni & Quinn 2001; Antón et al. 2011; Roni et al. 2015), boulder placement (Roni et al. 2002; Vehanen et al. 2003), limited pool excavation (Summers et al. 2008) or the creation of small islands (Vehanen et al. 2003; Boavida et al. 2011), among others. Furthermore, a recent review by Whiteway et al. (2010) showed that the installation of instream structures (deflectors, weirs, boulders, cover structures and large woody debris) often results in an increase in pool area, mean depth and shelter, along with higher salmonid abundance. They also observed that large salmonids generally experienced a greater increase in abundance (Gowan & Fausch 1996; Rosi-Marshall et al. 2006). In any case, instream habitat enhancement should only be employed after restoring the river natural processes or in particular cases where short-term habitat improvements are needed (Roni et al. 2002).

This investigation has highlighted the importance of evaluating the potential effectiveness of proposed management actions over physical habitat, prior to their actual implementation (Thompson 2006; Stewart et al. 2009). Physical habitat simulation has proven to be a useful tool to perform previous assessment by the quantification of habitat improvement under several management actions. Nevertheless, it is also important to evaluate the actual effectiveness of the applied management measures, after their implementation. This can be done by the assessment of their effects over physical habitat, fish populations or other elements of the fluvial ecosystem (Whiteway et al. 2010).

In the second case study presented in this dissertation (chapter 6, Gortázar et al. 2011), physical habitat was assessed at the mesohabitat scale, focusing on the brown trout population inhabiting in the river Tajuña (Guadalajara, central Spain), which is altered by agricultural uses of the riverside. Long reaches of this river are straightened, with important channel entrenchment and incision, along with degradation of riparian vegetation, flow depletion and regulation. Physical habitat has been modeled by means

General Discussion of the MesoHABSIM approach (Parasiewicz 2001, 2007a), with some adaptations to this particular case study. MesoHABSIM is based on the characterization of habitat attributes at the mesohabitat scale: types of hydromorphologic units (HMUs), types of cover (which have a key importance for salmonids, Armstrong et al. 2003; Ayllón et al. 2009), depth, water velocity, substrate. To build a hydromorphological model of the river, physical habitat characterization was carried out in 16 survey reaches (1-2 km long) representative of the 16 segments in which river Tajuña was stratified. A biological model for brown trout was also constructed, based on literature and calibrated with electrofishing data. The combination of the hydrological and the biological models provided the habitat model, which defines the suitability of each HMU for trout as not suitable, suitable or optimal.

Following the stream classification by Rosgen (1994, 1996) all the segments of the river Tajuña corresponded to the Gc stream type, characterised by an entrenched channel, low width/depth ratio, moderate sinuosity and low gradient. Restoration measures were designed to convert the impaired segments to C type (with meandering channel, low entrenchment and riffle-pool sequences) as proposed by Rosgen (1997) for the restoration of incised rivers. The designed restoration consists in excavating the stream banks to widen the river channel, using the gained material to raise the streambed. Besides, in the rectified reaches, a more sinuous channel would be constructed instead of the current straight channel. These measures should be reinforced by distancing land uses from the river, reforesting the stream banks with native riparian species and implementing a natural-like flow regime.

These restoration measures were simulated by means of MesoHABSIM and the amount of effective habitat in the restored scenario was compared with the prior conditions. The consequences of restoration on brown trout habitat availability were evaluated by quantifying effective habitat before and after restoration. This analysis has permitted to identify in which sites the proposed restoration measures are worth implementing. In this case, since the objective was to improve the carrying capacity for brown trout in order to increase population abundance, three of the river segments have been proposed for restoration because it has been estimated that they would experience a considerable habitat improvement. The three reaches recommended for restoration are currently seriously altered (channel rectification and entrenchment, degradation of riparian vegetation) so simulation results were congruent with field observations.

The MesoHABSIM approach has been applied in several studies (review in Parasiewicz et al. 2013a) related to environmental flow assessment (Vezza 2010; Vezza et al. 2014b), flow management strategy (Parasiewicz 2008a), modeling fish habitat suitability (Vezza et al. 2014a) or river restoration through the identification of habitat deficits and potential improvement measures (Parasiewicz 2008b). Besides MesoHABSIM, there are other mesohabitat modeling approaches, such as Meso- CASiMiR (Eisner et al. 2005), the Norwegian meso-scale habitat classification method (Borsányi et al. 2004), the Unit Characteristic Method (UCM, Cramer & Ackerman 2009a, 2009b) or the Mesohabitat Evaluation Model (MEM, Hauer et al. 2009). Moreover, other investigations at the mesohabitat scale have addressed the relationships between habitat and fish abundance (Baran et al. 1996; Lamouroux et al. 1998; Alcaraz- Hernández et al. 2007, 2009; Costa et al. 2012), mesohabitat use by salmonids (Heggenes et al. 2002; Moir & Pasternack 2008) and by fish communities (Bain 1995), fish distribution related to biotic and abiotic factors (Vezza et al. 2013, 2015), flow

General Discussion management (Martínez-Capel et al. 2010) or methodological issues on mesohabitat inventories (Moir & Pasternack 2008; Alcaraz-Hernández et al. 2011; Mouton et al. 2011).

In summary, this investigation has highlighted the usefulness of physical habitat assessment at the mesohabitat scale to evaluate the potential effectiveness of planned restoration measures. This kind of assessment allows water managers to evaluate whether the restoration would be effective and to choose the appropriate stream reaches to restore. Hence, it is a useful tool for decision making in fluvial ecosystem management.

The methods used in both case studies are useful for physical habitat assessment and for informing decision making in the context of river habitat management. The main difference between them is the spatial scale they use, and this implies further differences. IFIM focuses on the microhabitat whereas MesoHABSIM studies the mesohabitat scale. In IFIM, microhabitat variations in depth, water velocity and substrate are simulated across different flow values, using specific hydraulic software. Conversely, MesoHABSIM cannot currently simulate how mesohabitats change with stream flow. This entails that multiple surveys of the same stream reaches (with different flows) have to be done in order to obtain flow-habitat relationships (Parasiewicz 2007a; Vezza 2010).

However, the larger spatial scale of MesoHABSIM requires a considerably smaller fieldwork effort to survey the same river length than microhabitat approaches (Parasiewicz 2007a; Vezza et al. 2013). This allows rivers to be surveyed for long reaches, large enough to be representative of instream habitats distribution (Vezza et al. 2013). Thus, the ability of MesoHABSIM to quickly collect detailed physical habitat data from long river reaches permits its effective application in large-scale projects with reasonable effort (Parasiewicz 2007a).

Another difference between both methods is that the mesohabitat approach uses larger scale variables and classes for some variables instead of the continuous variables and precise values used by microhabitat models. This can involve a loss of accuracy, but in broad-scale studies this potential accuracy loss is overcome by the use of habitat units more consistent with the scale of the study (Parasiewicz 2007a). Moreover, physical habitat assessment at the mesohabitat scale involves a larger range of habitat descriptors (e.g., types of HMUs, several types of shelter), enabling understanding of fish behavior at larger spatial scales (Vezza 2010).

In summary, despite the lower resolution of mesohabitat approaches they can provide larger coverage of surveyed rivers (Vezza 2010). According to Jewitt et al. (2001), sacrificing some detail can reveal larger ecological patterns representing whole system properties. Therefore, within the framework of habitat simulation, mesohabitat approaches such as MesoHABSIM are appropriate for large scale projects. Indeed, a comparative study by Parasiewicz and Walker (2007) showed that MesoHABSIM make better predictions of fish abundance than microhabitat models, and that the variation in suitable habitat availability within micro-scale models was greater than between micro- and meso-scale models.

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General Discussion

On the other hand, two-dimensional microhabitat models are able to accurately represent complex mosaics of depth and velocity distributions (Ghanem et al. 1996; Crowder & Diplas 2000) and thus they are well suited to study specific actions where micro-hydraulic patterns are important (Boavida et al. 2012).

To conclude, it is important to underline that any management measure over physical habitat should be integrated into a sound understanding of the river processes, which are dependent on the hydrology and geomorphology of the whole catchment (Hynes 1975; García de Jalón 1995; Kauffman et al. 1997; Maddock 1999; Hendry et al. 2003; Cowx & van Zyll de Jong 2004). Habitat restoration and management should be designed to reduce human impacts and to bring the river as close as possible to its natural state (in structure and functioning). If these objectives are achieved, the results may extend far beyond the improvement of salmonid populations and the whole river ecosystem would benefit (Hendry et al. 2003). From this broad-scale perspective, it should be highlighted the key influence of land uses on the fluvial ecosystems (Kauffman et al. 1997; Maddock 1999; Lehane et al. 2004), which entails that, in many cases, some kind of land-use management or regulation will be required to restore rivers or to prevent further damage (Hendry et al. 2003). It is essential to recognize the root causes of the deterioration and to treat them and not just the symptoms (Hynes 1975; García de Jalón 1995; Kauffman et al. 1997; Hendry et al. 2003). Moreover, preventing the degradation of streams is always more effective than repairing them once they are degraded (García de Jalón 1995).

River habitat restoration should pursue the naturalness of river ecosystems. This means that the river should be driven to its natural state or to a condition as natural as possible given the actual constraints (e.g., land and water uses). Therefore, restoration planning should define a “reference condition” for the river system, and managers should be aware of the kind of reference condition employed, because this term may have different meanings: minimally disturbed condition, historical condition, least disturbed condition, best attainable condition (Stoddard et al. 2006; Dufour & Piégay 2009). Ideally, reference condition would reflect the river functioning in the absence of significant human alteration. An understanding of how human activities impact river dynamics will permit the design of restoration actions to reduce human induced alteration and to bring the river closer to the reference condition (Dynesius & Nilsson 1994; Bernhardt et al. 2005; Parasiewicz 2007b).

Reference conditions can be defined by the observation of unaltered river sites which are comparable to the target site, or by gathering historical data from a date with less human pressure (e.g., Wolter et al. 2003). However, in many cases it is not possible to observe an unimpaired river reach which can serve as a reference, and the oldest historical data are frequently subsequent to human impacts (particularly in Europe) or they lack the necessary precision. Furthermore, climate change may also modify hydrological processes and thus it may cause shifts in the reference conditions (Logez & Pont 2012; Pont et al. 2015). Therefore, the establishment of reference conditions for a given target river is often a difficult and complex task. However, the combination of habitat modeling, in situ observations, historical data and expert judgement can help to define quantitative reference conditions for river restoration (Muxika et al. 2007; Parasiewicz 2007b; Alonso et al. 2011b).

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8. CONCLUSIONS / CONCLUSIONES

Conclusions / Conclusiones

8.1. Conclusions

- The results of this Thesis and the future verification of the hypotheses proposed here will help improve the knowledge on salmonid populations at the southern edge of their natural ranges.

Brown trout spawning:

- Brown trout spawning in the river Castril occurs from December until April with the maximum activity in February. This finding represents one of the most belated and protracted spawning periods reported within the natural range of the species. It is now known that this type of spawning period (belated and extended) is widespread throughout the rest of Andalusian populations.

- Spawning mean time in brown trout is explained by latitude, by a negative relationship: the lower the latitude, the later the spawning time (R2 = 62.8%). Altitude also shows a negative relationship with spawning mean time, although it was not significant with the available data.

- The duration of spawning in brown trout populations is partly explained by latitude, by a negative relationship: the lower the latitude, the longer the spawning period. However, this relationship is weaker (R2 = 24.4%) than the latitude-spawning mean time relationship.

- A long spawning period can be an advantage for survival of brown trout populations in unpredictable habitats, because it increases the probability that at least part of the annual recruitment survive, by avoiding the destructive effects of stochastic events (floods and droughts) occurring at the critical moments of the life cycle.

- The high rate of redd superimposition observed in the river Castril is not only caused by high density of spawners (excess of reproductive females and/or scarcity of suitable habitat).

- Brown trout females choose specific sites for redd construction instead of randomly dispersing over the suitable spawning habitat that is available. It is plausible that female spawners have some kind of preference for superimposing redds, and thus, the presence of prior redds is a factor that must be considered when assessing spawning site selection.

- In limestone streams such as Castril, unused gravels are very cohesive and hard to dig, and therefore redd superimposition may give female a significant advantage, because digging may be much more effortless in already used redd sites than in cohesive and highly embedded unused sites.

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Conclusions / Conclusiones

Physical habitat management:

- From a management point of view, it is important to forecast to some degree the results of planned measures over physical habitat, prior to their actual implementation. In this sense, two different approaches have been used for physical habitat assessment in southern salmonid populations, and both have proven useful to quantify the potential change in salmonid habitat availability and to estimate the outcome of proposed habitat measures.

- At the microhabitat scale, the IFIM approach along with a two dimensional hydraulic model (River2D), has estimated a very small and non significant increasing in habitat availability for Atlantic salmon in the river Pas (Cantabria) under the proposed habitat enhancement actions. Therefore it is not worth to implement these measures in this stream reach.

- A restoration scheme has been designed for the river Tajuña (Guadalajara), which is currently impacted by agricultural land use: channel rectification, entrenchment and incision, flow regulation and degradation of riparian vegetation. The MesoHABSIM approach has permitted to quantify, at the mesohabitat scale, the potential increasing in brown trout habitat availability under the proposed river restoration measures, and to identify in which sites the planned restoration is worth implementing.

- Despite the lower resolution of MesoHABSIM approach, it can provide large and cost-effective coverage of surveyed rivers, and therefore it is appropriate for large scale projects. On the other hand, IFIM-River2D approach is able to accurately represent complex mosaics of depths and velocities, and thus it is appropriate for the assessment of specific habitat actions where micro-hydraulic patterns become relevant.

- Any management measure over physical habitat should be integrated into a sound understanding of the hydrological and geomorphological processes of the whole catchment.

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Conclusions / Conclusiones

8.2. Conclusiones

- Los resultados de la presente Tesis y la futura comprobación de las hipótesis propuestas aquí, ayudarán a mejorar el conocimiento acerca de las poblaciones de salmónidos en el límite meridional de sus distribuciones naturales.

Reproducción de la trucha común:

- La freza de la trucha común en el río Castril ocurre desde diciembre hasta abril, con el máximo de actividad en febrero. Esto representa uno de los periodos de freza más tardíos y con mayor duración observados dentro de la distribución natural de la especie. Actualmente se sabe que este tipo de periodo reproductivo (retrasado y extendido) está generalizado en el resto de poblaciones andaluzas.

- La latitud explica la fecha media de reproducción de la trucha común, mediante una relación negativa: a menor latitud, la freza ocurre más tarde (R2 = 62.8%). La altitud también muestra una relación negativa con la fecha media de freza, pero esta relación no fue significativa con los datos disponibles.

- La latitud explica en parte la duración de la freza de la trucha común, mediante una relación negativa: a menor latitud, el periodo de freza es más largo. No obstante, esta relación es más débil (R2 = 24.4%) que la relación latitud-fecha media de freza.

- Un periodo de freza largo puede suponer una ventaja para la supervivencia de las poblaciones de trucha común en hábitats impredecibles, porque aumenta la probabilidad de que al menos una parte del reclutamiento anual sobreviva, al evitar los efectos destructivos de fenómenos estocásticos (crecidas y sequías) que puedan ocurrir en los momentos críticos del ciclo vital.

- La elevada tasa de solapamiento de frezaderos observada en el río Castril no se explica únicamente por una excesiva densidad de reproductores (exceso de hembras maduras y/o escasez de hábitat adecuado).

- Las hembras de trucha eligen lugares específicos para construir sus frezaderos en lugar de dispersarse aleatoriamente dentro del hábitat adecuado para la freza que tienen disponible. Es verosímil que las hembras tengan algún tipo de preferencia por solapar sus frezaderos y, por tanto, la presencia de frezaderos previos es un factor que debe ser tenido en cuenta a la hora de evaluar la elección de lugares de freza.

- En ríos calizos como el Castril, las gravas son muy cohesivas y difíciles de excavar, por lo que el solapamiento de frezaderos puede suponer una ventaja significativa para la hembra, porque la excavación resultará considerablemente

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Conclusions / Conclusiones

más fácil en sustratos removidos por la construcción de un frezadero anterior que en sustratos con gravas cohesivas que no han sido utilizados previamente.

Gestión del hábitat físico:

- Desde el punto de vista de la gestión, es importante predecir en algún grado los resultados de las medidas que se planifiquen sobre el hábitat físico, antes de su implementación real. En este sentido, se han empleado dos enfoques diferentes para la evaluación del hábitat físico en poblaciones meridionales de salmónidos, y ambos enfoques han mostrado su utilidad para cuantificar los cambios potenciales en la disponibilidad de hábitat de salmónidos y para estimar los resultados de las medidas propuestas.

- En la escala del microhábitat, la metodología IFIM junto con un modelo hidráulico bidimensional (River2D), ha estimado que el aumento en la disponibilidad de hábitat para el salmón en el río Pas (Cantabria) bajo las medidas de mejora del hábitat propuestas, sería muy pequeño y no significativo. Por tanto no sería eficaz implementar estas medidas en este tramo fluvial.

- Se ha diseñado un plan de restauración para el río Tajuña (Guadalajara), que actualmente sufre impactos por los usos agrícolas de sus riberas: rectificación, encajonamiento e incisión del canal fluvial, regulación de caudales y degradación de la vegetación riparia. La metodología MesoHABSIM ha permitido cuantificar, en la escala del mesohábitat, el aumento potencial de la disponibilidad de hábitat para la trucha bajo las medidas de restauración propuestas, e identificar en qué tramos fluviales la restauración planificada resultaría eficaz.

- La metodología MesoHABSIM, a pesar de su menor resolución, puede proporcionar una cobertura amplia y rentable (económicamente y en términos de esfuerzo) de los ríos muestreados, y por tanto resulta apropiada para proyectos de gran escala. Por otra parte, el método IFIM-River2D es capaz de representar con exactitud mosaicos complejos de profundidades y velocidades, por lo que es adecuado para la evaluación de acciones concretas sobre el hábitat en las que los patrones micro-hidráulicos sean relevantes.

- Cualquier acción o medida de gestión sobre el hábitat físico debe estar integrada en una sólida comprensión de los procesos hidrológicos y geomorfológicos del conjunto de la cuenca vertiente.

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9. HYPOTHESES FOR FURTHER RESEARCH / HIPÓTESIS PARA LA INVESTIGACIÓN FUTURA

Hypotheses for further research / Hipótesis para la investigación futura

9.1. Hypotheses for further research

Within the topic of brown trout spawning, two hypotheses have emerged from the work conducted in this Thesis. These hypotheses are yet to be tested:

- Hypothesis: Brown trout spawning duration is partly explained by habitat unpredictability by a positive relationship: the more unpredictable is the habitat, the longer the spawning period.

- Hypothesis: Brown trout females have a higher preference for superimposing redds in streambeds with cohesive and embedded substrates (such as many limestone streams) than in rivers with loose gravels.

9.2. Hipótesis para la investigación futura

A partir del trabajo realizado en esta Tesis, dentro del tema de la reproducción de la trucha común, han surgido dos hipótesis que deberán ser comprobadas en el futuro:

- Hipótesis: La duración de la freza de la trucha común se explica en parte por la impredecibilidad del hábitat mediante una relación positiva: cuanto más impredecible es el hábitat, más largo es periodo de freza.

- Hipótesis: Las hembras de trucha común tienen una preferencia por solapar sus frezaderos, que es mayor en cauces con sustratos cohesivos (como muchos ríos calizos) que en ríos con sustratos de gravas sueltas.

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