Limnetica, 29 (2): x-xx (2011)Limnetica, 30 (2): 379-392 (2011)c© Asociacion Iberica de Limnologıa, Madrid. Spain. ISSN: 0213-8409

Physical habitat assessment in the Tajuna river (Spain) by means ofthe MesoHABSIM approach

Javier Gortazar1,2,∗, Piotr Parasiewicz3, Carlos Alonso-Gonzalez2 and Diego Garcıa de Jalon2

1 Ecohidraulica S.L. C/ Rodrıguez San Pedro 13. 28015 Madrid. www.ecohidraulica.com.2 Grupo de Investigacion Hidrobiologıa. Departamento de Ingenierıa Forestal. Escuela Tecnica Superior deIngenieros de Montes. Universidad Politecnica de Madrid. Av. Ramiro de Maeztu s/n. 28040 Madrid.3 Rushing Rivers Institute. 50 Two Ponds Road. Belchertown, MA. 01007 USA. www.RushingRivers.org.

∗ Corresponding author: jgortazar@ecohidraulica.com2

Received: 22/6/2010 Accepted: 22/3/2011

ABSTRACT

Physical habitat assessment in the Tajuna river (Spain) by means of the MesoHABSIM approach

Physical habitat was assessed in the Tajuna river (Tagus basin, Spain) by means of the MesoHABSIM approach. Long reachesof the Tajuna river are altered by agricultural use of the riverside. The main impacts are river rectification (straightening),channel entrenchment and incision, and degradation of riparian vegetation, along with important flow depletion and regulation.To our knowledge, this is the first application in Spain of MesoHABSIM, which is a physical habitat model based on theidentification of habitat attributes – depth, water velocity, substrate, types of hydromorphologic units (HMU), and types ofcover – on the mesohabitat scale.The river was stratified into 16 segments with similar habitat characteristics. Mesohabitats were mapped in one representativesite (1-2 km long) within each segment to provide a hydromorphologic model of the river. Biological models were developedfor fry, juvenile, and adult brown trout. To do this, preliminary models were generated based on literature about trout habitatrequirements, and then they were calibrated with electrofishing data. These models were applied to the hydromorphologicmodel of the river to quantify the available habitat for brown trout in the current conditions. Finally, restoration action wasdesigned to decrease channel entrenchment, increase river sinuosity, and recover its riparian vegetation. The physical changesafter restoration were estimated by expert opinion, and the quantification of the available habitat was done with MesoHABSIMat each site. These results can be used to select the segments that are the best candidates for restoration.

Key words: MesoHABSIM, mesohabitat, hydromorphologic unit (HMU), physical habitat, restoration, brown trout.

RESUMEN

Evaluacion del habitat fısico en el rıo Tajuna (Espana) mediante la metodologıa MesoHABSIM

Se ha evaluado el habitat fısico en el rıo Tajuna (cuenca del Tajo, Espana) mediante la metodologıa MesoHABSIM. Una parteimportante del rıo Tajuna esta alterada por los usos agrıcolas de las riberas. Los principales impactos son la rectificacion,el encajonamiento e incision del cauce, y la degradacion de la vegetacion riparia, junto con una importante extraccion yregulacion del caudal.Hasta donde conocemos, esta es la primera aplicacion de MesoHABSIM en Espana. Se trata de un modelo de habitatfısico, basado en la identificacion de los atributos del habitat –profundidad, velocidad del agua, sustrato, tipo de unidadhidromorfologica (HMU) y tipo de cobertura o refugio– en la escala del mesohabitat.Se estratifico el rıo en 16 segmentos con caracterısticas similares de habitat. En cada segmento se muestrearon losmesohabitats en un tramo representativo de 1-2 km de largo, construyendo ası un modelo hidromorfologico del rıo. Segeneraron modelos biologicos para alevines, juveniles y adultos de trucha comun. Para ello, se construyeron unos modelospreliminares a partir de bibliografıa acerca de los requerimientos de habitat de la trucha, y despues se calibraron con datosobtenidos mediante pesca electrica. Estos modelos fueron aplicados al modelo hidromorfologico para cuantificar el habitatdisponible para la trucha comun en las condiciones actuales. Finalmente se diseno una accion de restauracion con el objetivode disminuir el encajonamiento del cauce, aumentar su sinuosidad y recuperar su vegetacion riparia. Los cambios tras la

380 Gortazar et al.

restauracion se estimaron por opinion de experto, y la evaluacion del habitat resultante se realizo mediante MesoHABSIM.Estos resultados pueden emplearse para elegir los segmentos mas apropiados para realizar la restauracion propuesta.

Palabras clave: MesoHABSIM, mesohabitat, unidad hidromorfologica (HMU), habitat fısico, restauracion, trucha comun.

INTRODUCTION

There are several tools for the assessment ofphysical habitat in rivers on a microhabitat scale,such as PHABSIM (Bovee, 1982), RHYHAB-SIM (Jowett, 1989), and EVHA (Ginot, 1995).Habitat studies in a river channel can be appliedon different scales of analysis, such as the micro-habitat or mesohabitat scales. For instance, mi-crohabitat scale models within the IFIM frame-work have been broadly used in Spain, primarilyfor the establishment of ecological flow regimes.

The approach used in this work, MesoHAB-SIM, involves the use of the mesohabitat scale indata acquisition and analysis, and this defines itscharacteristics and applications. MesoHABSIMis an approach for modelling physical habitat inrivers. It allows a user to quantify the availablehabitat for selected fauna under specific environ-mental circumstances, and it can be used to simu-late diverse scenarios, such as river alterations orrestoration measures (Parasiewicz et al., 2009).The rationale of MesoHABSIM is the recogni-tion that fauna reacts to the environment on dif-ferent scales related to the size and mobility ofthe species as well as to the time of use. There-fore, the effort is focused on the habitat character-isation on the meso-scale level. Meso-scale unitscan be defined as areas where an animal can beobserved for a significant portion of its diurnalroutine, and it roughly corresponds with the con-cept of “functional habitat” (Kemp et al., 1999).Observation on the meso-scale can be expected toprovide meaningful clues about an animal’s selec-tion of living conditions (Hardy & Addley, 2001).

This paper describes the first application inSpain of the MesoHABSIM approach. It wasan eminently practical application with the fol-lowing objectives: to evaluate the current habi-tat availability for the brown trout in the Tajuna

river and to quantify the habitat improvementthat would be obtained if a specific restorationaction was implemented.

METHODS

The work presented here applies the underlyingconcepts of the MesoHABSIM approach and itsfundamental methods, but it is a simplificationof the standard MesoHABSIM process, primar-ily due to budget constraints. The main modifica-tions from the usual process are the following:(1) although MesoHABSIM usually considershabitat use within a range of flow values, in thiswork, the habitat was analysed under a singleflow magnitude; (2) our study focuses on a singlespecies, the brown trout (Salmo trutta L.), whileMesoHABSIM permits consideration of the en-tire fish community; and (3) the biological mod-els that define the kind of habitat used by thefish are normally generated by sampling fish with“electrogrids” (Bain et al., 1985) in several meso-habitats and processing data with logistic regres-sion analysis, but in this work, we have builtliterature-based models and calibrated them withthe available electrofishing data.

Study site

The Tajuna river is located in the province ofGuadalajara, Spain, and it flows westward intothe Jarama river, which belongs to the Tagusbasin. The study site was a 116 km long seg-ment of the Tajuna river between its source andthe confluence with its tributary, the San Andresriver. Brown trout (Salmo trutta L.) is the mostabundant species inhabiting the study site. Otherautochthonous species in the fish community arebarbel (Barbus bocagei Steindachner), Iberian nase

MesoHABSIM application in the Tajuna river 381

(Pseudochondrostoma polylepis Steindachner),Iberian chub (Squalius pyrenaicus Gunther),“bermejuela” (Achondrostoma arcasii Stein-dachner), “calandino” (Iberocypris alburnoidesSteindachner) and Iberian loach (Cobitis palu-dica de Buen). The exotic species present aregudgeon (Gobio lozanoi Doadrio & Madeira) andrainbow trout (Oncorhynchus mykiss Walbaum).

The most obvious alterations of the Tajunariver are caused by agriculture on the marginsof the river course. This land use is responsi-ble for several stressors that configure the cur-rent geomorphology of the river (Palmer et al.,2010). The main alterations are the rectificationof several reaches that reduce sinuosity, the in-cision and entrenchment of the riverbed, whichmakes the channel deep and homogeneous, andthe degradation of the riparian vegetation, alongwith an important flow depletion and regulationto irrigate the adjacent crops.

Segments 2, 7, 8 and 12 (see below) do notsuffer from these alterations, and their physicalhabitat is little altered or unaltered because theyflow through narrow valleys with poor accessi-bility where it has not been possible to developagricultural fields. These segments show a diver-sity of HMU types, with notable numbers of rif-fles and rapids. Cascades, backwaters and sidearms are also present, thus providing a diversityof microhabitats. Therefore, restoration was nota priority in these segments.

MesoHABSIM application

A detailed description of the MesoHABSIM ap-proach is provided in Parasiewicz (2007) andParasiewicz et al. (2009). It follows the typ-ical structure of habitat models described byParasiewicz & Dunbar (2001) and is an aggre-gation of three models: (1) a hydromorphologicmodel that describes the spatial mosaic of fish-relevant physical features, (2) a biological modeldescribing habitat use by animals, and (3) a habi-tat model quantifying the amount of usable habitat.

The study area was stratified in 16 segments(Table 1) based on a reconnaissance survey ofthe whole river and on the interpretation of aerialphotographs and focusing on alterations and land

Table 1. Characterisation of the 16 segments in the Tajunariver: Length, mean altitude and gradient. Caracterizacion delos 16 segmentos definidos en el rıo Tajuna: longitud, altitudmedia y pendiente.

SegmentLength(km)

Altitude(m a.s.l.)

Gradient(%)

1 14.77 1164 0.342 16.36 1134 0.713 18.56 1094 0.414 12.19 1073 0.275 19.86 1046 0.206 15.62 1030 0.217 12.01 1001 0.388 14.46 1897 0.479 11.85 1887 0.1610 15.55 1863 0.5211 13.79 1839 0.4712 12.75 1824 0.4713 19.03 1798 0.4214 14.08 1775 0.2015 19.00 1752 0.4216 13.60 1718 0.23

use changes along the river. Within each segment,one representative site (1-2 km long) was cho-sen for the mesohabitat survey, accounting for16.7 % of the total study area length.

In each site, a mesohabitat survey was per-formed in June and July, 2009. Every hydro-morphologic unit (HMU) within the site wasidentified, and its outlines were drawn as geo-referenced polygons on a Pocket PC, using GISsoftware and aerial photographs. The physical at-tributes were estimated for each HMU, and thedata were entered into a GIS table associatedwith the corresponding polygon. The physical at-tributes considered were the HMU type, the covertypes (using three categories: absent, present andabundant), and the proportions of depth, meancolumn velocity and substrate classes within theunit (for a description of the physical attributes,see Parasiewicz, 2007). Depth, mean column ve-locity and estimated substrate were measuredin seven random locations within each HMU.Measurements for depth and water velocity weretaken with a Dipping Bar (Jens, 1968). Sub-strate definitions were based on the substrateclassification system according to the AustrianStandard ONORM 6232 (1995).

382 Gortazar et al.

During the mesohabitat surveys, the flow relatedto the catchment area in each site was 0.0011 m3

s−1 km−2 (0.1 ft3 s−1 mile−2), similar to the usualflow during summer. Therefore, our results refersolely to moderate flow conditions and cannot beapplied to spates or extreme droughts.

We generated the biological models by con-sidering the habitat use of three developmen-tal stages of brown trout: fry (0+ age class,fork length < 102.5 mm), juvenile (1+ ageclass, 102.5 mm < FL < 182.5 mm) and adult(older fish, FL > 182.5 mm). Preliminary biolog-ical models were generated based on the litera-ture and were calibrated with electrofishing dataobtained in the Tajuna river. To specify the affin-ity of fish presence with depth, water velocityand substrate, we used suitability curves devel-oped by Bovee (1978, category I curves as de-fined by Armour et al., 1984, i.e., based on lit-erature sources or professional opinion), Raleighet al. (1986, category I) and Heggenes (1990,category III or preference curves). To define thebrown trout preferences for HMU and covertypes, the review on brown trout habitat require-ments by Armstrong et al. (2003) was consulted.When the literature provided different values fordistinct parts of the year or flow magnitudes, weused those that referred to either summer or mod-erate flow conditions because our study focusedon habitat availability with moderate flow, whichwe assume occurs during the summer and, someyears, even during a part of the spring and fall.The preliminary model consisted of certain HMUtypes, intervals of depth and velocity, and featuresof 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 isnot a curve or a continuous variable but intervals orfeatures to be considered in the habitat evaluation.

Once the preliminary biological models weregenerated, they were calibrated with quantitativeelectrofishing data to maximise the model’s abil-ity to explain the observed trout densities. Elec-trofishing data were obtained during July 2009,in a period of flow similar to that of the meso-habitat survey. Fish were sampled with pulsed di-rect current in twelve river reaches, each c. 100 mlong, located within the mapped sites. Trout den-

sity was estimated with the three-pass removalmethod, following Carle & Strub (1978). To cal-ibrate the model for each developmental stage,we performed a linear regression analysis in thesites with available electrofishing data, relatingthe following two variables: brown trout den-sity (individuals m−2) and proportion of effectivehabitat within the electrofished area related to themapped surface. Effective habitat is an aggrega-tion of suitable and optimal habitats with dif-ferent weights to assure a high contribution ofthe “optimal” habitat: Effective habitat = Suitablehabitat + 1.5 Optimal habitat. Model calibrationconsisted of an iterative process: for each itera-tion, one class of one variable (a single depth,a velocity or substrate class, or a single HMUor cover type) was included in the model or ex-cluded from it, and the linear regression anal-ysis between trout density and effective habitatwas calculated again. After each change, if theregression 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 bythe fish (biological model) provided the habitatmodel,which classifies the suitability of eachHMUinto three categories: not suitable, suitable andoptimal (see Parasiewicz 2007 for more detail).

Physical habitat restoration

To propose specific restoration measures to im-prove habitat availability, it is necessary to iden-tify which habitat attributes should be increasedor decreased. To make these ecologically baseddecisions, it was necessary to identify the cur-rent habitat deficits for the three life stages ofbrown trout and to determine at which life stagehabitat is more limited.

For the identification of habitat deficits, thecurrent physical habitat characteristics (Table 2)were compared with the needs of brown trout asdetailed in the biological models. Habitat deficitswere identified by visual inspection of Table 2.

To identify the life stage with the most lim-ited habitat, we compared the current propor-tions of available habitat for each developmen-

MesoHABSIM application in the Tajuna river 383

Table 2. Physical habitat characteristics in the current summer conditions. For HMU and cover types, the proportion of HMUswith the attribute is given (cover types are not mutually exclusive). For depth, velocity and substrate, the proportion of each class inthe study area is given. Superscripts indicate suitable attributes for adult (A), juvenile (J) or fry (F), based on the biological models.Caracterısticas del habitat fısico actual en verano. Para el tipo de HMU y refugio, se muestra la proporcion de HMUs con el atributo(los tipos de refugio no se excluyen mutuamente). Para la profundidad, velocidad y sustrato, se muestra la proporcion de cada claseen el area de estudio. Los superındices indican los atributos adecuados para el adulto (A), juvenil (J) o alevın (F), segun los modelosbiologicos.

Depth (cm) Prop. Velocity (cm s–1) Prop. Substrate Prop.

< 25 0.29 < 15A, J 0.37 PelalA 0.2725-50J, F 0.30 15-30A, J, F 0.24 AkalF 0.0450-75A, J, F 0.17 30-45A, J, F 0.19 MicroLithalF 0.2075-100A, J 0.08 45-60J, F 0.10 MesoLithalA, J 0.14100-125A 0.05 60-75 0.05 MacroLithalF 0.06> 125A 0.10 75-90 0.03 MegaLithalA, J, F 0.06

90-105 0.01 Phytal 0.13> 105 0.02 Psammal 0.08

Xylal 0.01Sapropel 0.01Debris 0.00Detritus 0.00Gigalithal 0.00

HMU type Prop. Cover type Prop.

RapidsA, J, F 0.05 BouldersA, F 0.33

RiffleA, J, F 0.08 UndercutA, J, F 0.33PoolA, J 0.18 Shallow marginF 0.41

Side armF 0.00 Submerged Veg.A 0.50

BackwaterF 0.00 RipRap 0.04Cascade 0.00 Overhanging Veg. 0.71Ruffle 0.18 Canopy Cover 0.76Run 0.20 Woody Debris 0.40Glide 0.29 Low Gradient 0.89Plungepool 0.00

tal stage and the habitat proportions needed bya brown trout population with a balanced agestructure. The population structure observed inthe segments without physical habitat alterations(sites 2, 7, 8 and 12) was considered to be thebalanced or “ideal” population structure. Elec-trofishing data from these sites were pooled, andthe linear regression between age class densityand age was computed. The slope of this regres-sion (mortality) was used to derive the habitatdistribution of a balanced population structure af-ter a correction for the relative amount of habi-tat 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.

We have selected restoration measures withthe purpose of increasing the availability of

brown trout habitat. Using the stream classifica-tion by Rosgen (1994), all the segments corre-sponded to class G (Gc stream type, as the riverslope is lower than 2 %). The G stream typeis characterised by an entrenched channel, lowwidth/depth ratio and moderate sinuosity. Basedon this stream classification, we proposed to im-plement restoration measures that would convertthe current G stream type to a C type, as proposedby Rosgen (1997) for the restoration of incisedrivers. The C stream type is characterised by alow gradient (< 2 %), a meandering channel, lowentrenchment, and riffle-pool morphology.

To implement this restoration, land use shouldbe distanced from the river, thus opening a bufferthat would allow the river to develop its natu-ral processes (Gonzalez del Tanago & Garcıa de

384 Gortazar et al.

Jalon, 2007). The streambank walls should be ex-cavated, thereby widening the river channel, andthe material should be used to raise the bed eleva-tion. This would reduce the bank height and ero-sion rate by decreasing the stress near the banks(Rosgen, 1996), thus reducing the sediment in-put into the channel. In the rectified reaches, amore sinuous channel should be constructed in-stead of the current straight channel. In generalterms, the river channel would become wider andshallower. Afterwards, the river banks should bereforested with indigenous riparian species, anda flow regime as close to natural as possibleshould be implemented.

A simulation of this restoration action in theTajuna river was done by means of the Meso-HABSIM approach and using the software Sim-Stream 7.1. The restoration was only simulatedin altered sites (excluding the unimpaired seg-ments 2, 7, 8 and 12). The simulation has beenconducted by the generation of new hydromor-phologic models of the restored river segments.The new models were created by changing someattributes of the current models in the way thatthey would be expected to change if the restora-tion was implemented, based on the literature andexpert opinions, and also considering the rela-tive presence of the attributes in the unimpairedsites. The changes in the hydromorphologic mod-els for the restored scenario were the following(Grant et al., 1990; Rosgen, 1994; 1996; 1997;Palmer et al., 2010). First, the area occupied byruns and glides, which are currently the mostabundant HMU types, was reduced and partly re-placed by riffles and some rapids, that is, 33 %of the surface occupied by runs and 29 % of theglide area were substituted by riffles (85 % ofthe changed area) and rapids (15 %). Also, thepelal and phytal substrates were reduced (15 %of pelal and 35 % of phytal points) and were re-placed by the substrate types that were beneaththe fines and plants in the same proportion thatthey were observed in the unaltered sites: akal(3 %), microlithal (31 %), mesolithal (38 %) andmacrolithal (28 %).Additionally, all the riprapwasremoved, and submerged vegetation was reduced(51 % of HMU surface with this attribute). In thereaches with degraded riparian vegetation, canopy

shading (36 % of the mapped area) and over-hanging vegetation (25 %) were increased. Shal-low margins were also increased (40 %), as partsof the river would become wider and shallower.

The depth would decrease in general due tothe new channel width, but the implementationof a natural flow regime could potentially com-pensate for that by providing higher flow duringsummer, as currently there is a substantial waterwithdrawal in the summer for irrigation. Becauseonly one type of flow condition has been sampledso far, the depths were not changed in the model.After the application of the attribute changes de-scribed above, the model was run again.

To evaluate whether the proposed restora-tion would provide a significant increase in theamount of available brown trout habitat andwhich would be the most appropriate segmentsfor restoration, the amount of effective habitatwas compared between the current summer con-ditions and the restored scenario.

Table 3. Biological models generated for brown trout in theTajuna river. Attributes used by each developmental stage forthe variables HMU type, cover type, depth, water velocity andsubstrate. If the five conditions were satisfied, the HMU wasevaluated as optimal habitat. If three conditions (four for theadult) were satisfied, the HMU was evaluated as suitable habi-tat. Modelos biologicos generados para la trucha comun en elrıo Tajuna. Atributos utilizados por cada estado de desarrollopara las variables tipo de HMU, tipo de cobertura, profundi-dad, velocidad del agua y sustrato. Si se cumplen las cincocondiciones la HMU se evalua como habitat optimo. Si secumplen tres condiciones (cuatro para el adulto) la HMU seevalua como habitat adecuado.

Variable Adult Juvenile Fry

HMU type RapidsRifflePool

RapidsRifflePool

RapidsRiffleSide armBackwater

Cover type BouldersSubmerged veg.Undercut

Undercut BouldersShallow marginUndercut

Depth > 50 cm 25-100 cm 25-75 cm

Watervelocity

0-45 cm s−1 0-60 cm s−1 15-60 cm s−1

Substrate PelalMesolithalMegalithal

MesolithalMegalithal

AkalMicrolithalMacrolithalMegalithal

MesoHABSIM application in the Tajuna river 385

RESULTS

The electrofishing data used for model calibrationprovided a brown trout total catch ranging from 0to 100 individuals and trout density varying from0 to 0.289 individuals m−2. The calibration of thebiological models (Table 3) yielded significantlinear regressions between trout density and theproportion of effective habitat (Fig. 1). The habi-

JuvenileR

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Figure 1. Validation of the biological models for adult, juve-nile and fry brown trout. Linear regression analysis betweendensity (individuals m–2) and proportion of effective habitatrelated to the mapped surface within the electrofished area(n = 12). Validacion de los modelos biologicos para el adulto,juvenil y alevın de trucha comun. Regresion lineal entre la den-sidad (individuos/m2) y la proporcion de habitat efectivo res-pecto a la superficie muestreada, dentro de la zona en que serealizo pesca electrica (n = 12).

tat availability obtained by the application of thebiological models explained 40 % of the browntrout density for both adults and fry ( p < 0.05).Juvenile habitat availability explained 52 % ofthe juvenile trout density ( p < 0.01).

The application of the biological models inthe mapped sites allowed us to quantify theamount of optimal and suitable habitat for eachdevelopmental stage at every site (Fig. 2). Opti-mal habitat was only relevant for adults, while fryand juvenile habitat availability was mainly madeup of HMUs that were classified as suitable.

The visual inspection of Table 2 allowed usto identify the main habitat deficits in the currentconditions. 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 andfor shallow-slow HMU types such as backwatersand side arms. Regarding cover types, Table 2shows that the increase of undercut banks, boul-ders and shallow margins would improve habitatsuitability. There is an excess of the pelal (which

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Figure 2. Proportion of suitable (open bars) and optimal(solid bars) habitat area related to the total mapped area in eachsite for adult, juvenile and fry brown trout. Proporcion de lasuperficie de habitat adecuado (barras blancas) y optimo (bar-ras negras) respecto a la superficie total muestreada en cadatramo, para el adulto, juvenil y alevın de trucha comun.

386 Gortazar et al.

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Figure 3. Comparison for the whole study area between theproportion of available habitat for each developmental stage inthe current conditions (open bars) and the habitat needed to sus-tain a balanced population structure (solid bars). Comparacionpara todo el ambito de estudio, entre la proporcion de habitatdisponible para cada estado de desarrollo en las condicionesactuales (barras blancas) y el habitat necesario para manteneruna poblacion de trucha con una estructura equilibrada (bar-ras negras).

is acceptable for adults, but not for juveniles andfry) and phytal substrates, but there is little akal,macrolithal and megalithal available. The aug-mentation of these attributes would lead to anincrease in habitat availability for brown trout.

Figure 3 shows that fry habitat seemed tobe the limiting factor for the development of abalanced population structure in the Tajuna river,as a balanced or “ideal” population requires morefry habitat than is currently available. Therefore,for the design of the restoration measures, wefocussed on the habitat needs of brown trout fry.

The results of habitat availability in the re-stored scenario were compared with the cur-rent conditions. The effective habitat (Fig. 4) in-creased after restoration in almost all instances,except for the adult habitat in some cases (sites1, 5, 6 and 11). However, the increases weresubstantial only at sites 1, 4 and 16. At site1, the large increase in juvenile and especiallyfry habitat compensated for the slight reductionin adult habitat. Site 4 also showed a great in-crease in habitat availability, which was dramaticfor fry habitat. At site 16, the largest increasewas in juvenile habitat availability.

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Figure 4. Effective habitat for each developmental stage inthe current conditions (open bars) and after restoration (solidbars). Proportion of effective habitat area related to the totalmapped area in each site. Habitat efectivo para cada estadode desarrollo en las condiciones actuales (barras blancas) ytras la restauracion (barras negras). Proporcion de superficiede habitat efectivo respecto a la superficie total muestreada encada tramo.

DISCUSSION

We showed how physical habitat can be assessedusing the MesoHABSIM approach, and we havedeveloped a model of the river that allows usto quantify the available habitat for brown troutwhen using a single flow value. After this pro-cess, the habitat model can be used for a vari-ety of purposes, including the identification offish habitat deficits, river restoration planning orenvironmental impact assessment. As a demon-stration of the possibilities of MesoHABSIM, wehave evaluated how available trout habitat would

MesoHABSIM application in the Tajuna river 387

change if a specific restoration action was imple-mented in the Tajuna river.

The quantification of effective habitat beforeand after restoration (Fig. 4) allows natural re-sources managers to evaluate the consequencesof restoration on habitat availability for each lifestage of brown trout. This information permitsevaluating at which sites the proposed restorationmeasures are worth implementing, depending onthemanagement objectives. For instance, if the goalis to increase the carrying capacity for brown troutand thus to improve population abundance, then wecan recommend restoration of sites 1, 4 and 16because it has been estimated that they will expe-rience an important physical habitat improvement.The recommendation of restoration at these threesites focuses on the improvement of fry habi-tat because it is currently the limiting lifestage for the development of a balanced browntrout population. Thus, it is plausible that thewhole population would increase after restora-tion even if the adult habitat slightly decreasesin some places, as is the case of site 1. Further-more, the augmentation of fry habitat availabilitymay have a positive effect on population abun-dance, as it has been shown that high recruit-ment induces numerically strong year-classes(Lobon-Cervia, 2005; 2009). The three sites rec-ommended for restoration (1, 4 and 16) cur-rently show serious alterations such as channelrectification, entrenchment and the degradationof riparian vegetation, so our field observationsare congruent with the simulation results.

Regarding the approach used in this work,the use of the mesohabitat scale may involvea loss of accuracy because the meso-scale useslarger scale variables and classes for some vari-ables (e.g., depth and water velocity) instead ofthe continuous variables used by other models.However, in broad-scale studies, the presumedaccuracy loss is overcome by the use of habi-tat units that are more consistent with the scaleof the study (Parasiewicz, 2007). If the meso-habitat scale sacrifices some detail, however, itcan reveal larger spatial and temporal ecologi-cal patterns that represent system properties (Je-witt et al., 2001). Furthermore, MesoHABSIMis able to quickly collect detailed information

about physical conditions from long river reaches(Parasiewicz, 2007), and thus, it can be effec-tively applied in large-scale projects with reason-able effort. It can easily simulate large scale man-agement actions, allowing for the prediction ofeffects of measures such as dam removals or ex-tensive channel restoration.

The simulation of a restoration scenario bymodifying a physical habitat model, based on ex-isting knowledge of fluvial processes, has beendone before by Garcıa de Jalon & Gortazar(2007) using the microhabitat model River2D(Steffler, 2000). In this kind of work, it is im-portant to include enough precision when es-timating how habitat attributes will change af-ter restoration to obtain reliable results from thesimulation. One good possibility is to create areference image of the river in natural condi-tions if the restoration is designed to move thesystem to its natural state.

In our case, it was not possible to create a ref-erence image of the river in natural conditionsbecause there is a lack of historical informationabout the river habitat, and the observed geo-morphological alterations originated long beforethe oldest aerial photographs were taken. More-over, physical habitat in the four unaltered seg-ments cannot be used as reference for the restof the segments because the two groups of seg-ments do not share the same characteristics innatural conditions. The main difference is in thevalley type: the four natural segments are in nar-row valleys, and their course is constrained bythe hill slopes; the rest of the segments flowthrough wider valleys, where sinuosity in natu-ral conditions may be greater. For this reason,we have estimated how habitat attributes wouldchange in the restored scenario based on existingknowledge, as explained above.

To obtain reliable results from physical habi-tat models, another key issue is the capacity ofthe biological models to reflect actual habitat useby fish in the specific study sites. The biologi-cal models attained provide the best possible ex-planation of trout density by habitat availabilityin this particular case study. The biological mod-els generated in this work should not be called“preference” models (or “category III” models,

388 Gortazar et al.

Bovee, 1986). Rather, they are “use” models be-cause they reflect the fish’s use of a range of habi-tats that include unpaired and altered habitats.

The range of depth used by fish, accordingto the Tajuna model, is congruent with the cat-egory I curves by Bovee (1978) for the three lifestages, as the Tajuna depth range always includesthe depths with maximum probability in theBovee model (Fig. 5). The discrepancy is higherwith the category I curves given by Raleighet al. (1986), especially for the adult, as ourmodel uses any depth higher than 50 cm, whileRaleigh employs a low probability of use (< 0.4)for depths higher than 120 cm.

The range of water velocity used by adultsin our model includes all the values that have aprobability higher than 0.5 in the Bovee model.Tajuna velocity use by juveniles includes valuesup to 60 cm/s, while both the Bovee and Raleighprobability of use is decreasing quickly at thispoint, reaching approximately 0.3 at 60 cm/s. Inour model, fry do not use velocities lower than15 cm/s. This is congruent with the Raleigh model,which gives low suitability for low velocities, butnot with Bovee model, which reports maximum

probability for velocity lower than approximately30 cm/s. The disuse of low water velocity byfry in the Tajuna model may be partly causedby the competition with bigger trout in thosemicrohabitats, displacing fry to other places withhigher current and thus higher energetic cost.

In the Tajuna model, fry use the akal (meandiameter between 2-20 mm) and microlithal(20-63 mm) substrate classes, and juveniles andadults use the mesolithal (63-200 mm). This re-sult is congruent with both the Raleigh and Boveemodels, which give high probability (> 0.8) forgravel (2-64 mm) use by fry and for cob-ble/rubble (64-250 mm) use by juveniles andadults. Use of the pelal (silt, clay) class byadults in our model also agrees with the Raleighmodel (a probability of 1 for silt). The great-est discrepancies in substrate use between thethree models are the following: (1) in our model,the megalithal (> 400 mm) class is used by thethree life stages, and fry also use macrolithal(200-400 mm), while both the Raleigh and Boveemodels show low probability of use (< 0.2) forboulders (250-4,000 mm); and (2) Raleigh andBovee report gravel use by juveniles and sand

Figure 5. Comparison for the variables depth and water velocity between the biological model of the Tajuna river (grey horizontalbar) and two category I models, reported by Bovee (1978, thick black line) and Raleigh et al. (1986, thin black line). Comparacion,para las variables profundidad y velocidad del agua, entre el modelo biologico del rıo Tajuna (barra horizontal gris) y dos modelosde categorıa I publicados por Bovee (1978, lınea negra gruesa) y Raleigh et al. (1986, lınea negra delgada).

MesoHABSIM application in the Tajuna river 389

use by adults and fry, while our model doesnot consider use in these instances. These differ-ences can be related to habitat availability in thestudy sites and to a secondary relevance of sub-strate in the microhabitat selection because otherfish habitat studies in Mediterranean rivers (in-cluded Tajuna) have reported that depth and ve-locity are more important (Martınez-Capel et al.,2009). The use of univariate models is also a lim-itation that can be overcome within the Meso-HABSIM approach using multivariate models ofmesohabitat variables developed for brown troutin Mediterranean rivers (Mouton et al., 2011).

Regarding HMU type, our model indicatesthat rapids and riffles are used by the three de-velopmental stages, which reflects the suitabil-ity of these HMU types for brown trout (Maki-Petays et al., 1997; Roussel & Bardonnet, 1997).This usage may be partly caused by the scarcityof rapids and riffles in the Tajuna river, which areactively used where present, reflecting the impor-tance of instream habitat heterogeneity (Roussel& Bardonnet, 2002). Pools are used by juvenilesand adults, reflecting the suitability of deep ar-eas 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 wherethey can avoid competition with larger trout.This observation is congruent with the findingsof Maki-Petays et al. (2000), who showed thatfry prefer low-velocity refuges.

Cover is an important element of instreamhabitat and has been shown to be related tohabitat use by trout (Binns & Eiserman, 1979;Heggenes, 1988a). In the Tajuna model, the threelife stages are favoured by the presence of under-cut banks, which seems reasonable because thistype of cover provides good shelter for browntrout (Garcıa de Jalon & Schmidt, 1995). Fryand adults use the boulder cover type, which pro-vides for interstitial space and low water velocitymicroniches (Heggenes, 1988b; Heggenes et al.,1993; Bardonnet & Heland, 1994). Fry also useHMUs with shallow margins, reflecting the needsof small trout for shallow and slow-flowing areas.

Therefore, the biological models used in thiswork are realistic, as trout chose habitat features

that are reasonable in light of the existing knowl-edge. Furthermore, the biological models havebeen calibrated with abundance data collectedin the field, including both altered and unpairedhabitats, and thus, they maximise the relationshipbetween habitat availability and brown trout den-sity in this particular case study.

In this work, we have applied the underlyingconcepts of MesoHABSIM and its fundamen-tal methods. We have shown how this approachcan be used to simulate the effects of restorationmeasures on instream habitat before the restora-tion is implemented. This information allows wa-ter managers to evaluate whether the restora-tion measures would be effective and to choosethe appropriate sites to implement them. TheMesoHABSIM approach permits the simulationof diverse changes in physical habitat, such asriver alterations or restoration measures, at thecatchment scale, and therefore, it is an effi-cient tool for decision making in the manage-ment of rivers and watersheds.

ACKNOWLEDGEMENTS

This work was funded by the Regional Govern-ment of Castilla-La Mancha, Spain (Junta de Co-munidades de Castilla-La Mancha), within theframework of the project “Hydrobiological studyof the Tajuna river in the province of Guadala-jara” (Estudio Hidrobiologico del rıo Tajuna en laprovincia de Guadalajara). We kindly acknowl-edge two anonymous reviewers for their impor-tant comments and suggestions, which helpedus to significantly improve the manuscript. Wealso thank Joe Rogers (Rushing Rivers) forhis helpful advice on the use of Sim-Stream(www.sim-stream.com) software, database man-agement and many other matters throughout theprocess and David Hernandez Lopez for his ex-cellent fieldwork and comments.

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