A review of ecological models for brown trout: towards a new demogenetic model


B. M. Frank, Earth and Life Institute, Université catholique de Louvain, Croix du Sud 2, Box 14, B-1348 Louvain-la-Neuve, Belgium. E-mail: beatrice.frank@uclouvain.be


Abstract –  Ecological models for stream fish range in scale from individual fish to entire populations. They have been used to assess habitat quality and to predict the demographic and genetic responses to management or disturbance. In this paper, we conduct the first comprehensive review and synthesis of the vast body of modelling literature on the brown trout, Salmo trutta L., with the aim of developing the framework for a demogenetic model, i.e., a model integrating both population dynamics and genetics. We use a bibliometric literature review to identify two main categories of models: population ecology (including population dynamics and population genetics) and population distribution (including habitat–hydraulic and spatial distribution). We assess how these models have previously been applied to stream fish, particularly brown trout, and how recent models have begun to integrate them to address two key management and conservation questions: (i) How can we predict fish population responses to management intervention? and (ii) How is the genetic structure of fish populations influenced by landscape characteristics? Because salmonid populations tend to show watershed scale variation in both demographic and genetic traits, we propose that models combining demographic, genetic and spatial data are promising tools for improving their management and conservation. We conclude with a framework for an individual-based, spatially explicit demogenetic model that we will apply to stream-dwelling brown trout populations in the near future.


Over the past three decades, ecological models have increasingly been applied in the management and conservation of freshwater fish populations (e.g., Larkin 1978; Barnard et al. 1995; Whipple et al. 2000; Filipe et al. 2004; Einum et al. 2008). These models have been used to predict abundance and population growth rate (Lebreton 2006), population subdivision and gene flow (Pearse & Crandall 2004), habitat quality (Anderson et al. 2006) and large-scale distribution (Ahmadi Nedushan et al. 2006). Thus, ecological models are increasingly being used to guide management decisions, especially for threatened and exploited species. Models for stream fish have ranged in scale from individuals to populations; early models focused on physical habitat (Bovee 1982) and on population-level responses such as demographic (Elliott 1994) and genetic variations (Wright 1969). Later developments included functions to predict spatial patterns of either fish occurrence (e.g., Stanfield & Gibson 2006) or genetic diversity (e.g., Dillane et al. 2008). Most recently, individual-based simulation techniques have been developed to explicitly include individual variation, as well as demographic and environmental stochasticity, into ecological models (e.g., Strand & Niehaus 2007; Landguth & Cushman 2010; Schumaker 2011).

The rapid expansion in ecological modelling parallels technological advances in field methods (i.e., individual tagging and tracking), computing, and genetic analyses. Stream ecologists are now poised to model and to better understand how the interaction of demographic and genetic factors influences the distribution and abundance of fish. This is a daunting task, however, because of the difficulty of assimilating the broad knowledge base that has developed, somewhat independently, in a number of subdisciplines (e.g., demography, genetics, physical habitat simulations, etc.). In this paper, we assemble and synthesise for the first time the vast body of ecological modelling literature on stream fish, focusing on the brown trout, Salmo trutta L. Our goal is to demonstrate how these previously independent models have begun to be integrated, and how further integration will allow ecologists to better address important questions for the management and conservation of freshwater fish.

The brown trout is a species particularly well-suited for serving as a model organism for both management and conservation. Its quantitative ecology, from individual habitat selection to population dynamics, is as well known as that of any stream fish (e.g., Elliott 1994; but see numerous others). It is also one of the vertebrate species presenting the highest degree of intraspecific biological diversity including strong genetic and phenotypic variation among populations (Laikre 1999; Bernatchez 2001). This genetic variability among brown trout populations is attributable to several factors, including the effects of recent glaciations, the physical characteristics of the hydrographic systems and local differentiation without barriers owing to territorial behaviour and strong homing instinct (i.e., individuals return to spawn in the stream in which they were born) (Laikre 1999; Antunes et al. 2006). These factors result in limited gene flow among populations, producing partially isolated random mating units both within and among watersheds (Ferguson 1989). This often leads to adaptation to local environmental conditions, changes in genetic structure and the development of unique demographic traits like morphology, feeding preferences and life history strategies (Laikre 1999; Klemetsen et al. 2003; Ferguson 2006). The importance of this intraspecific diversity for fisheries management has long been recognised (e.g., Ricker 1972; Spangler et al. 1981; Taylor 1991), and most recently Schindler et al. (2010) identified its crucial role in providing ecosystem services. Human activities, including environmental degradation (pollution, altered flow, fragmentation of habitat), fishing and fish stocking (Laikre 1999; Cowx & Gerdeaux 2004; Dudgeon et al. 2006), have resulted in a loss of intraspecific diversity of brown trout, and many remaining native stocks are now faced with a medium-term risk of extinction (e.g., Laikre 1999; Caudron et al. 2010).

In recent years, considerable attention has been paid to the ecology of brown trout. Population dynamics of a number of populations were reviewed (e.g., Roussel & Bardonnet 2002; Klemetsen et al. 2003; Lobon-Cervia 2005, 2007; Northcote & Lobon-Cervia 2008), and molecular genetic techniques have contributed to improving the basic genetic information available on population structure, both at large (Bernatchez 2001) and at small spatial scales (Hansen et al. 2001; Sonstebo et al. 2007b). Recent advances in fish marking techniques, spatial analyses and molecular genetics have allowed the establishment of long-term data sets of demography and population genetic structure based on sampling of individual fish (e.g., Hansen et al. 2002; Lobon-Cervia & Rincon 2004; Fraser et al. 2007; Haugen et al. 2008). Although advances have been achieved in both population dynamics and population genetics models, comprehensive models that integrate these two characteristics of salmonid populations are still in their infancy (Palsboll et al. 2007). Because brown trout tend to show both demographic variation and genetic diversity at the watershed scale, models that link population dynamics and genetics (i.e., demogenetics) across spatial scales hold great promise in providing insights into their management and conservation.

In this review, we show how ecological models have previously been applied to stream fish populations, particularly brown trout, and how the most advanced models might be integrated to develop a new demogenetic model. First, we conduct a bibliometric review of the literature on ecological models for brown trout, and we identify and summarise two categories and four types of models that have been developed over the past 30 years. Then, we demonstrate how some of these models have already begun to be integrated, and how they might be used to address two key questions relevant to the conservation and management of stream fish: (i) How can we predict fish population responses to management intervention? and (ii) How is the genetic structure of fish populations influenced by landscape characteristics? We conclude with a framework for a new demogenetic model for brown trout populations that will further integrate existing theory through the use of an individual-based, spatially explicit platform.


We developed a bibliometric approach to identify the main topics addressed by scientific publications of ecological models for brown trout. We followed two steps: the selection of publications, and the construction of the corresponding directed network of citations.

In the first step, we used the ISI Web of KnowledgeSM search engine to identify key publications, the references they cited and those that cited them (Table 1). We selected 68 publications (from 2003 to 2008), among which 59 articles, seven proceedings papers, one editorial material and one review, and their 2964 citations (from 1980 to 2009).

Table 1.   Search criteria for the selection of publications devoting to the brown trout.
FeatureSearch criterion
Publication databaseWeb of Science. Source: ISI Web of Knowledge CrossSearch, available at: http://www.isiknowledge.com/WOS, Accessed: January 20, 2009
Key wordsTopic=(salmo* AND trutta AND model* AND (river* OR stream* OR basin* OR freshwater*) NOT sea* NOT lake* NOT (salmonel* OR chemi* OR tox* OR ion* OR metal*))
Time spanFrom 2003 to 2008
Citation databaseScience Citation Index Expanded (SCI-EXPANDED), from 1980 to 2009

In the second step, a directed network was built. First, an adjacency matrix was created. This latter consists of a symmetric matrix whose row and column elements reflect vertices (nodes), and each cell contains the value of an edge (interaction). In our case, the vertices are the 68 publications and their 2964 references, while the edges are constituted with ones and zeros, each ‘one’ representing a link between two vertices (here, 3567 links). Then, the 3032 × 3032 adjacency matrix was converted into an edge list matrix, using the ‘Network’ R package (Butts et al. 2008). It is a rectangular matrix with two columns. We chose a directed network and, in this case, each row element represents an edge and contains two vertices that are linked by this edge: the first one is taken to be the tail vertex of the edge and the second one is the edge’s head vertex (Butts 2008). Finally, importing the edge list matrix into the Cytoscape software (Shannon et al. 2003) allowed us to visualise the network properly by using, for instance, the ‘Organic layout’. To facilitate the analysis of this network, its size was reduced: vertices with four or less input edges were deleted. We obtained a network made of 59 vertices (44 publications plus 15 citations) and 104 directed edges (Fig. 1). This allowed us to identify the major categories of models, the types of models within them and links among these.

Figure 1.

 The reduced network used to categorise ecological models of stream-living brown trout populations. This network, made of 59 vertices (44 publications, labelled A, plus 15 references, labelled R) and 104 directed edges, was visualised with the Cytoscape software using an ‘Organic layout’. Three clusters were identified, corresponding to the following categories: brown trout population ecology (Cluster One, light-gray nodes), population distribution (Cluster Two, dark-gray nodes), and a combination of the two previous categories (Cluster Three, white nodes).

Results: literature review and synthesis

Two main references display the largest number of input edges in the directed network (Fig. 1): R766 and R355, with 19 and 12 inputs, respectively. The former reference, R766, is the well-known book ‘Quantitative Ecology and the Brown Trout’ (Elliott 1994). This book covers various subjects such as the global success of the brown trout species, growth and energetics, population dynamics of adults and juveniles, ecology and genetics. The latter reference, R355, is the report ‘A Guide to Stream Habitat Analysis Using the Instream Flow Incremental Methodology’ (IFIM) (Bovee 1982). This guide explains how to assess riverine habitats and impact of disturbances on these habitats and fish that live there.

From these two main references, three clusters are observed in the network. Cluster One, population ecology, is in the left of Fig. 1 (light-gray nodes), centred on Elliott (1994). It comprises 18 articles and six references, mostly addressing the ecology of brown trout populations (Table 2). Cluster Two, population distribution, is in the right of Fig. 1 (dark-gray nodes), centred on Bovee (1982). It comprises 19 articles and seven references that address either trout local habitat preferences or their distribution at the catchment scale (Table 3). Cluster Three (white nodes in Fig. 1) comprises seven articles linking Clusters One and Two. In this cluster, a number of publications represent recent attempts to link population ecology and population distribution (Table 4).

Table 2.   Vertices belonging to the ‘Population ecology’ cluster.
VerticesFirst author (year)Publication’s title
R0479Cattaneo (2002)The influence of hydrological and biotic processes on brown trout (Salmo trutta) population dynamics
R1355Jenkins (1999)Effects of population density on individual growth of brown trout in streams
R1358Jensen (1999)The functional relationship between peak spring floods and survival and growth of juvenile Atlantic salmon (Salmo salar) and brown trout (S. trutta)
R1662Lobon-Cervia (2004)Environmental determinants of recruitment and their influence on the population dynamics of stream-living brown trout S. trutta
R1965Nehring (1993)Determination of population-limiting critical salmonid habitats in Colorado streams using the physical habitat simulation system
R2180Poff (1997)The natural flow regime: a paradigm for river conservation and restoration
A02Dauwalter (2008)Distribution modelling to guide stream fish conservation: an example using the mountain sucker in the Black Hills National Forest, USA
A03Budy (2008)Exploring the relative influence of biotic interactions and environmental conditions on the abundance and distribution of exotic brown trout (S. trutta) in a high mountain stream
A15Vincenzi (2008a)The role of density-dependent individual growth in the persistence of freshwater salmonid populations
A19Kristensen (2008)Variation in growth and aggression of juvenile brown trout (S. trutta) from upstream and downstream reaches of the same river
A22Matulla (2007)Assessing the impact of a downscaled climate change simulation on the fish fauna in an Inner-Alpine River
A26Zorn (2007)Influences on brown trout and brook trout population dynamics in a Michigan river
A29Vincenzi (2007)Density-dependent individual growth of marble trout (Salmo marmoratus) in the Soca and Idrijca river basins, Slovenia
A32Schager (2007)Status of young-of-the-year brown trout (Salmo trutta fario) in Swiss streams: factors influencing YOY trout recruitment
A33Weber (2007)Spatio-temporal analysis of fish and their habitat: a case study on a highly degraded Swiss river system prior to extensive rehabilitation
A38Leprieur (2006)Hydrological disturbance benefits a native fish at the expense of an exotic fish
A41Gregersen (2006)Egg size differentiation among sympatric demes of brown trout: possible effects of density-dependent interactions among fry
A42Lobon-Cervia (2006)Instability of stream salmonid population dynamics under strong environmental limitations – a reply
A43Daufresne (2006)Population fluctuations, regulation and limitation in stream-living brown trout
A47Johansen (2005)Relationships between juvenile salmon, S. salar L., and invertebrate densities in the River Tana, Norway
A51McRae (2005)Factors influencing density of age-0 brown trout and brook trout in the Au Sable River, Michigan
A57Yamamoto (2004)Genetic differentiation of white-spotted charr (Salvelinus leucomaenis) populations after habitat fragmentation: spatial-temporal changes in gene frequencies
A58Koizumi (2004)Metapopulation structure of stream-dwelling Dolly Varden charr inferred from patterns of occurrence in the Sorachi River basin, Hokkaido, Japan
A66Brannas (2003)Influence of food abundance on individual behaviour strategy and growth rate in juvenile brown trout (S. trutta)
Table 3.   Vertices belonging to the ‘Population distribution’ cluster.
VerticesFirst author (year)Publication’s title
  1. GIS, geographic information systems; PIT, passive integrated transponder.

R0153Armstrong (2003)Habitat requirements of Atlantic salmon and brown trout in rivers and streams
R0357Bovee (1986)Development and evaluation of habitat suitability criteria for use in the instream flow incremental methodology
R0360Bovee (1998)Stream habitat analysis using the instream flow incremental methodology
R0831Fausch (1981)Competition between brook trout (Salvelinus fontinalis) and brown trout (Salmo trutta) for positions in a Michigan stream
R1057Guay (2000)Development and validation of numerical habitat models for juveniles of Atlantic salmon (Salmo salar)
R1175Heggenes (1996)Habitat selection by brown trout (S. trutta) and young Atlantic salmon (S. salar) in streams: Static and dynamic hydraulic modelling
R1558Lamouroux (1999)Fish habitat preferences in large streams of southern France
A05Clark (2008)Spatial distribution and geomorphic condition of fish habitat in streams: an analysis using hydraulic modelling and geostatistics
A10Mouton (2008)Optimisation of a fuzzy physical habitat model for spawning European grayling (Thymallus thymallus L.) in the Aare river (Thun, Switzerland)
A11Ovidio (2008)Regulated discharge produces substantial demographic changes on four typical fish species of a small salmonid stream
A20Cucherousset (2007)Stable isotope evidence of trophic interactions between introduced brook trout S. fontinalis and native brown trout S. trutta in a mountain stream of south-west France
A23Dolinsek (2007)Assessing the effect of visual isolation on the population density of Atlantic salmon (S. salar) using GIS
A25Mouton (2007a)Concept and application of the usable volume for modelling the physical habitat of riverine organisms
A28Jowett (2007)A comparison of composite habitat suitability indices and generalized additive models of invertebrate abundance and fish presence-habitat availability
A30Enders (2007)Comparison between PIT and radio telemetry to evaluate winter habitat use and activity patterns of juvenile Atlantic salmon and brown trout
A44Thurow (2006)Utility and validation of day and night snorkel counts for estimating bull trout abundance in first- to third-order streams
A49Franco (2005)Effects of biotic and abiotic factors on the distribution of trout and salmon along a longitudinal stream gradient
A50Pont (2005)Modelling habitat requirement of European fishes: do species have similar responses to local and regional environmental constraints?
A52Imre (2005)Moon phase and nocturnal density of Atlantic salmon parr in the Sainte-Marguerite River, Quebec
A54Jones (2004)Resource selection functions for age-0 Arctic grayling (Thymallus arcticus) and their application to stream habitat compensation
A56Vilizzi (2004)Assessing variation in suitability curves and electivity profiles in temporal studies of fish habitat use
A60Nykanen (2004)Transferability of habitat preference criteria for larval European grayling (Thymallus thymallus)
A61Booker (2004)Application of physical habitat simulation (PHABSIM) modelling to modified urban river channels
A62Lopes (2004)Hydrodynamics and water quality modelling in a regulated river segment: application on the instream flow definition
A64Statzner (2003)Contribution of benthic fish to the patch dynamics of gravel and sand transport in streams
A67Mengin (2002)ProCURVE: software to calculate habitat preferences of aquatic organisms
Table 4.   Vertices connecting the ‘Population ecology’ and the ‘Population distribution’ clusters.
VerticesFirst author (year)Publication’s title
A01Gouraud (2008)Long-term simulations of the dynamics of trout populations on river reaches bypassed by hydroelectric installations – Analysis of the impact of different hydrological scenarios
A16Louhi (2008)Spawning habitat of Atlantic salmon and brown trout: general criteria and intragravel factors
A17Ohlund (2008)Life history and large-scale habitat use of brown trout (S. trutta) and brook trout (Salvelinus fontinalis) – Implications for species replacement patterns
A21Vincenzi (2008b)Potential factors controlling the population viability of newly introduced endangered marble trout populations
A34Hauer (2007)The importance of morphodynamic processes at riffles used as spawning grounds during the incubation time of nase (Chondrostoma nasus)
A53Nislow (2004)Testing predictions of the critical period for survival concept using experiments with stocked Atlantic salmon
A65Capra (2003)A population dynamics model and habitat simulation as a tool to predict brown trout demography in natural and bypassed stream reaches

Clusters One and Two are linked to two broad categories of ecological models for brown trout: population ecology models and population distribution models (Table 5). Each category is further divided into two subcategories; population ecology models include population dynamics and population genetics models, and population distribution models include habitat–hydraulic and spatial distribution models. Initially, these population ecology and population distribution models were developed largely independently, but more recently there have been efforts to integrate them. Spatial dynamics models and landscape genetics models are promising approaches that link either population dynamics models or population genetics models across spatial scales; spatial dynamics models emphasise the importance of temporal changes in population size and age structure, and spatial variability in stream habitats; landscape genetics models allow for studying the effects of environmental features on the genetic processes regulating a population. Below, we first review population ecology models and population distribution models. Then, we review the two additional model types: spatial dynamics and landscape genetics models. We also introduce individual-based simulation techniques, which have become an integral component of these latter models.

Table 5.   The six types of ecological models for stream fish that we identify based on our literature review. The study goals and parameters estimated are described for each of them.
 Population dynamics modelsPopulation genetics models
Study goalShort- and long-term changes in the size and age structure of a populationPopulation-level effects of genetic phenomena such as segregation, recombination, transposition and mutation
Estimated parametersAbundance, population growth rateDegree of population differentiation, population of origin, number of populations, past and current gene flow, effective population size
 Habitat–hydraulic modelsSpatial distribution models
Study goalHabitat characteristics and preferences of stream fish populationsRelationship between organisms occurrence and catchment characteristics
Estimated parametersHabitat suitability index in relation with stream dischargeHabitat suitability maps
 Spatial dynamics modelsLandscape genetics models
Study goalLimitation of population density by demographic processes, influences of flow on demographic ratesInteractions between landscape features and micro-evolutionary processes
Estimated parametersAbundance, population growth rate, habitat selection and suitabilityLocations of genetic discontinuities, genetic distance and connectivity

Through this synthesis of the literature, we identify the study goals and estimated parameters associated with each of the six model types we describe (Table 5). We focus on models and methods that have been developed for stream-dwelling brown trout and use examples from other taxa in cases where models for brown trout are lacking. We do not consider models designed for multiple species. A comprehensive list of models and corresponding methods can be found in Table 6. For each model type, we provide one example of a research question that might be answered by using the model.

Table 6.   Types of ecological models, software in which they are implemented, and examples of applications for brown trout (Salmo trutta) or other salmonid species.
Model types/MethodsSoftwareApplications examples
  1. 1Wolfram Research Inc., Champaign, Illinois, USA. http://www.wolfram.com/mathematica

  2. 2R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org

  3. 3Norsys Software Corp., Vancouver, Canada. http://www.norsys.com/netica

  4. 4Lumina Decision Systems, Denver, Colorado, USA. http://www.lumina.com/why-analytica

  5. 5MWH Soft Ltd., Wallingford, Oxfordshire, UK. http://www.mwhsoft.com

  6. 6U.S. Army Corps of Engineers, Hydrologic Engineering Center, Davis, California, USA. http://www.hec.usace.army.mil/software/hec-ras

  7. 7SPSS Inc., Chicago, Illinois, USA. http://www.spss.com

  8. 8StatSoft Inc., Tulsa, Oklahoma, USA. http://www.statsoft.com

  9. 9Minitab Inc., State College, Pennsylvania, USA. http://www.minitab.com

  10. 10SAS Institute Inc., Cary, North Carolina, USA. http://www.jmp.com

  11. 11TIBCO Software Inc., Palo Alto, California, USA. http://spotfire.tibco.com/products/s-plus/statistical-analysis-software.aspx

  12. 12Salford Systems Inc., San Diego, California, USA. http://salford-systems.com/cart.php

  13. 13MathWorks Inc., Natick, Massachusetts, USA. http://www.mathworks.com/matlab

  14. 14isee systems Inc., Lebanon, New Hampshire, USA. http://www.iseesystems.com/softwares/Education/StellaSoftware.aspx

  15. 15Exeter Publishing Ltd., Setauket, New York, USA. http://www.exetersoftware.com/cat/ntsyspc/ntsyspc.html

  16. ABC, approximate Bayesian computation.

Population dynamics models
e.g., ‘What is the size of a fish population and how does it vary over time?’
 Stock-recruitment modelsEquations resolvingElliott 1994; Bell et al. 2000; Lobon-Cervia & Rincon 2004
 Matrix projection modelsMATHEMATICA1Sabaton et al. 1997; Charles et al. 1998; Gouraud et al. 2001; Daufresne & Renault 2006
 Integral projection modelsR2No S. trutta application found but see Fukuwaka & Morita 2008
 Bayesian belief networksNETICA3Lee & Rieman 1997; Rieman et al. 2001
ANALYTICA4Borsuk et al. 2006; Burkhardt-Holm 2008
 State-space modelsWinBUGS (Lunn et al. 2000)No S. trutta application found but see Rivot et al. 2004
Population genetics models
e.g., ‘How many genetically distinct fish populations are present in a system and what are the interactions among them?’
 F-statisticsARLEQUIN (Excoffier et al. 2005; Excoffier & Lischer 2010)Carlsson & Nilsson 2000; Ostergaard et al. 2003; Apostolidis et al. 2008
FSTAT (Goudet 1995)Carlsson & Nilsson 2000; Jensen et al. 2005a; Antunes et al. 2006; Hansen et al. 2007; Sonstebo et al. 2007a,b; Susnik et al. 2007; Heggenes et al. 2009; Vilas et al. 2010
GENEPOP (Raymond & Rousset 1995; Rousset 2008)Estoup et al. 1998; Lehtonen et al. 2009
GENETIX (Belkhir et al. 2004)Aurelle & Berrebi 1998; Campos et al. 2006
NEGST (Chakraborty et al. 1982)Bouza et al. 1999, 2001; Corujo et al. 2004
 Classification and clustering methodsGENECLASS (Cornuet et al. 1999; Piry et al. 2004)Estoup et al. 1998; Ostergaard et al. 2003; Corujo et al. 2004; Heggenes & Roed 2006; Sonstebo et al. 2007b; Wollebaek et al. 2010
STRUCTURE (Pritchard et al. 2000)Ayllon et al. 2006; Sonstebo et al. 2007a; Massa-Gallucci et al. 2010; Wollebaek et al. 2010
 Parentage analysisCOLONY (Wang 2004; Jones & Wang 2010b)Carlsson 2007; Serbezov et al. 2010a
PAPA (Duchesne et al. 2002)Duchesne et al. 2008
PARENTE (Cercueil et al. 2002)No S. trutta application found but see Vandeputte et al. 2006
PEDAGREE (Coombs et al. 2010)No S. trutta application found but see Hudy et al. 2010
 Ne – Heterozygote excess methodLuikart & England (Luikart & England 1999)Luikart & England 1999
 Ne – Linkage disequilibrium methodsLDNe (Waples & Do 2008)Massa-Gallucci et al. 2010
ONeSAMP (Tallmon et al. 2008)Wollebaek et al. 2010
 Ne – Temporal methodsMLNE (Wang 2001; Wang & Whitlock 2003)Ostergaard et al. 2003; Jensen et al. 2005a; Campos et al. 2007; Fraser et al. 2007; Hansen et al. 2007
TempoFs (Jorde & Ryman 2007)Heggenes et al. 2009
TM3 (Berthier et al. 2002)Hansen et al. 2002; Campos et al. 2007
 Migration and gene flow estimatorsBAYESASS (Wilson & Rannala 2003)Hansen et al. 2006, 2007; Apostolidis et al. 2008; Wollebaek et al. 2010
IM, IMa (Hey & Nielsen 2004, 2007)No S. trutta application found but see Nikolic et al. 2009; Pavey et al. 2010
LAMARC (Kuhner 2006)Carlsson 2007; Susnik et al. 2007
MIGRATE (Beerli & Felsenstein 2001; Beerli 2006)Campos et al. 2006; Fraser et al. 2007; Hansen et al. 2007
 Forward-time simulation modelsEasyPOP (Balloux 2001)No S. trutta application found but see Castric et al. 2002; Gomez-Uchida et al. 2008; Whiteley et al. 2010
FPG (Hey 2004)No salmonid application found
SFS-CODE (Hernandez 2008)No salmonid application found
 Coalescent simulation modelsMS (Hudson 2002)No salmonid application found
SEQ-GEN (Rambaut & Grassly 1997)Cortey et al. 2009
SIMCOAL (Excoffier et al. 2000; Laval & Excoffier 2004)No salmonid application found
DIY ABC (Cornuet et al. 2008)No S. trutta application found but see Nikolic et al. 2009
 Quantitative genetics: animal modelMCMCglmm R package (Hadfield 2010)Serbezov et al. 2010b
VCE (Groeneveld 1994)No S. trutta application found but see Martyniuk et al. 2003; Wilson & Rannala 2003; Norris 2004; Perry et al. 2004, 2005
DFREML (replaced by WOMBAT) (Meyer 1988, 2007)No S. trutta application found but see Hard et al. 1999; Rogers et al. 2002; Garant et al. 2003; Araneda et al. 2005; Paez et al. 2010
Hydraulic models
e.g., ‘How can we predict water depth and velocity throughout a stream reach?’
 One-dimensional modelsISIS Flow5Lopes et al. 2004
HEC-RAS6Borg & Roy 2006; Shieh et al. 2007
 Two- and three-dimensional modelsRiver2D (Steffler et al. 2006)Alfredsen et al. 2004; Hayes et al. 2007; Clark et al. 2008
SSIIM (Olsen 2010)Halleraker et al. 2003; Alfredsen et al. 2004; Booker et al. 2004
Habitat suitability models
e.g., ‘What is the habitat suitability index of a fish population?’
 Empirical preference curvesBovee’s model (Bovee 1982)Belaud et al. 1989; Souchon et al. 1989; Lamouroux & Capra 2002; Lamouroux & Jowett 2005; Ovidio et al. 2008
Raleigh’s model (Raleigh et al. 1986)Wesche et al. 1987
 Preference functionsVVF (Ortigosa et al. 2000)No salmonid application found
HABSCORE (Milner et al. 1993)Barnard et al. 1995
Habitat–hydraulic models
e.g., ‘What are the habitat preferences of a fish population in relation to stream discharge?’
 Suite of numerical modelsPHABSIM (Waddle 2001)Harris & Hubert 1992; Nehring & Anderson 1993; Sabaton et al. 1997; Van Winkle et al. 1998; Gibbins & Acornley 2000; Spence & Hickley 2000; Ayllon et al. 2010
 Models derived from PHABSIMRHABSIM (Payne 2005)Lopes et al. 2004
RHYHABSIM (Jowett 2002; Clausen et al. 2004)Thorn & Conallin 2006
MesoHABSIM (Parasiewicz 2001, 2007)No salmonid application found
EVHA (Ginot 1995; Pouilly et al. 1995)Maridet & Souchon 1995; Lamouroux & Capra 2002; Roussel & Bardonnet 2002; Capra et al. 2003; Ovidio et al. 2008
 Fuzzy logic modelsCASiMiR-Fish (Jorde et al. 2000; Schneider & Jorde 2003)Jorde et al. 2001; Schneider et al. 2002
Spatial distribution models
e.g., ‘How are fish distributed across a watershed and what is the relationship between their occurrence and the environmental characteristics?’
 Linear regressionSPSS7McRae & Diana 2005; Schager et al. 2007; Weber et al. 2007
STATISTICA8Almodovar et al. 2006; Stanfield & Gibson 2006
 Multiple regressionMINITAB9Lehane et al. 2004
JMP10Jones et al. 2006
S-PLUS11Gevrey et al. 2003; Pont et al. 2005
Software not mentionedBaran et al. 1995; Jowett 1995; Baran et al. 1996; Creque et al. 2005; Zorn & Nuhfer 2007
 Discriminant analysisSTATISTICA8Teixeira & Cortes 2007
Software not mentionedEklov et al. 1999; Zorn & Nuhfer 2007
 Classification and regression treesCART12Steen 2008
STATISTICA8Stoneman & Jones 2000; Stanfield & Gibson 2006; Teixeira & Cortes 2007
 Artificial neural networksMATLAB13Lek et al. 1996; Reyjol et al. 2001; Leprieur et al. 2006
Software not mentionedBaran et al. 1996; Lek & Baran 1997; Gevrey et al. 2003
 Mantel testsECODIST (Goslee & Urban 2007)Cattaneo et al. 2003; Hitt & Angermeier 2008
 Canonical correspondence analysisCANOCO (ter Braak & Smilauer 2002)Weigel & Sorensen 2001; Teixeira et al. 2006
Spatial dynamics models
e.g., ‘How can we predict fish population responses to management intervention or environmental modifications?’
 Bioenergetic modelsFish Bioenergetics (Hanson et al. 1997)Dieterman et al. 2004
Hayes’ model (Hayes et al. 2000)Booker et al. 2004
Hughes’ model (Hughes & Dill 1990; Hughes 1992)Hughes 1998; Hughes et al. 2003; Hayes et al. 2007
Elliott & Hurley’s model (Elliott & Hurley 1999, 2000)Vik et al. 2001; Jensen et al. 2006; Johnson et al. 2006; Dineen et al. 2007
 Habitat suitability models + matrix modelsMODYPOP (Sabaton et al. 1997; Gouraud et al. 2001)Capra et al. 2003
Charles’ model (Charles et al. 1998)Charles et al. 2000
SALMOD (Williamson et al. 1993; Bartholow 1996)Hickey & Diaz 1999
 Spatially explicit stock-recruitment modelsSTELLA14Jessup 1998
 Spatially explicit matrix projection modelsMATLAB13, R2No S. trutta application found but see Letcher et al. 2007
 Spatially explicit integral projection modelsMATLAB13, R2No salmonid application found
Landscape genetics models
e.g., ‘How is the genetic structure of fish populations influenced by landscape characteristics?’
 Mantel testsGENEPOP (Raymond & Rousset 1995; Rousset 2008)Estoup et al. 1998; Carlsson & Nilsson 2000; Campos et al. 2006; Hansen et al. 2007
GENALEX (Peakall & Smouse 2006)Sonstebo et al. 2007a; Lehtonen et al. 2009
NTSYSpc15Bouza et al. 1999, 2001; Sonstebo et al. 2007b
FSTAT (Goudet 1995)Heggenes & Roed 2006
IBDWS (Jensen et al. 2005b)Vilas et al. 2010
 Regression analysisGENEPOP (Raymond & Rousset 1995; Rousset 2008)Estoup et al. 1998
 Evolutionary treesSTREAM TREES (Kalinowski et al. 2008)No S. trutta application found but see Kalinowski et al. 2008; Meeuwig et al. 2010
 Monmonier algorithmBARRIER (Manni et al. 2004)No S. trutta application found but see Dillane et al. 2008
 Canonical correspondence analysisCANOCO (ter Braak & Smilauer 2002)No S. trutta application found but see Angers et al. 1999; Costello et al. 2003
 Principal component analysisPCA-GEN (Goudet 1999)Lehtonen et al. 2009
 Multidimensional scalingViSta (Young et al. 2006)Hansen et al. 2002, 2007
 Landscape metricsFRAGSTATS (McGarigal et al. 2002)No S. trutta application found but see Le Pichon et al. 2006
 Spatial clusteringBAPS (Corander & Marttinen 2006)Sonstebo et al. 2007b; Vilas et al. 2010
GENELAND (Guillot et al. 2005)No S. trutta application found but see Dionne et al. 2008
TESS (François et al. 2006)No salmonid application found
SPAGeDi (Hardy & Vekemans 2002)No salmonid application found
 Coalescent population genetics modelsSPLATCHE (Currat et al. 2004; Ray et al. 2010)No salmonid application found
AQUASPLATCHE (Neuenschwander 2006)No salmonid application found
ABCtoolbox (Wegmann et al. 2010)No salmonid application found
Nonspatial individual-based models
e.g., ‘How can we simulate the evolution of a fish population in terms of demography, genetics, or both?’
 Population dynamics modelsVORTEX (Lacy 2000; Lacy et al. 2005)No S. trutta application found but see Sato & Harada 2008
 Forward-time population genetics modelssimuPOP (Peng & Kimmel 2005; Peng & Amos 2008)No salmonid application found
NEMO (Guillaume & Rougemont 2006)No salmonid application found
 Quantitative genetics modelsDunlop’s ecogenetic model (Dunlop et al. 2009)No S. trutta application found but see Thériault et al. 2008
Wang’s model (Wang & Hook 2009)No S. trutta application found but see Wang & Hook 2009
 Demogenetic modelsMETASIM (Strand 2002)No salmonid application found
Spatially explicit individual-based models
e.g., ‘How can we predict the response of a fish population to future changes in the environment?’
 Bioenergetic modelsAddley’s model (Addley 1993)Guensch et al. 2001; Addley 2006
inSTREAM (Railsback et al. 2009)Van Winkle et al. 1998; Railsback & Harvey 2002; Railsback et al. 2003
 Spatial dynamics modelsHexSim (Schumaker 2011)No salmonid application found
 Quantitative genetics modelsquantiNEMO (Neuenschwander et al. 2008a)No salmonid application found
 Landscape genetics modelsCDPOP (Landguth & Cushman 2010)No salmonid application found
 Demogenetic modelsKernelPOP (Strand & Niehaus 2007)No salmonid application found

Population ecology models

We subdivide population ecology models into two types: population dynamics models and population genetics models. Population dynamics models address short- and long-term changes in the size and age structure of a population; typical outputs from these models are predictions of abundance and population growth rate (e.g., Lebreton 2006). Population genetics models aim to understand and to predict the genetic structure of populations (i.e., their allele and genotype frequency distributions) taking into account ecological and evolutionary factors such as population size, patterns of mating, gene flow, genetic drift, mutation and natural selection (e.g., Hartl & Clark 1989; Allendorf & Luikart 2007).

Population ecology models are thus a means to explain changes occurring in the demographic or genetic structure of a population, caused either by dynamic processes (i.e., births, deaths and dispersal) or by micro-evolutionary processes (i.e., natural selection, genetic drift, gene flow and mutation). Population dynamics and population genetics models can be integrated with two other types of models, habitat–hydraulic and spatial distribution models, to obtain spatial dynamics models and landscape genetics models, respectively. These latter model types are both reviewed in the second section of this literature synthesis, and in the last section, we demonstrate how they can address two questions relevant to the management and conservation of freshwater fish.

Linking evolutionary and demographic processes in ecological models (i.e., demogenetic models) and applying these to understand the patterns of genetic variation in freshwater fish populations should provide new insights to address questions about their management and conservation. For instance, levels of fecundity, mortality, immigration and emigration may alter the degrees of genetic exchange among populations, and these are capable of feeding back into one another (Kool 2009). As suggested at the end of this literature review and synthesis, the use of individual-based simulation techniques should greatly facilitate the development of the demogenetics field and its associated demogenetic models.

Population dynamics models

Population dynamics models can be subclassified into either mathematical or statistical models. Mathematical population models are dynamic and deterministic, because they describe how a population changes over time, and they have no random component (Ellner & Guckenheimer 2006). Exponential (Malthus 1798) and logistic (Verhulst 1838) models of population growth are the simplest mathematical population models describing changes in a population’s size. They differ on their assumptions about the availability of resources and are rarely used today. Another class of mathematical population models are the stock-recruitment models (e.g., Ricker 1954; Beverton & Holt 1957), which give the number of fish expected to survive (the recruits) at a later time, as a function of the number of spawners (the stock) at a previous time. For instance, Elliott (1994) used a Ricker model to describe the number of recruits at the different stages of the life cycle of brown trout, given the number of eggs at the beginning of each year. The last class of mathematical population models we describe here is the matrix projection models (see Caswell 2001). Age-structured models developed by Leslie (1945) are the deterministic matrix models most commonly used in the literature, and several were developed for brown trout populations (e.g., MODYPOP: Sabaton et al. 1997; Gouraud et al. 2001; see also Table 6). The approach of Leslie was upgraded by classifying individuals into stages of development (Lefkovitch 1965) or size classes (Usher 1966) and was eventually generalised to consider any structuring factor (Caswell 2001). Recently, integral projection models, or IPMs, have been developed as a practical alternative to deterministic matrix models for structured populations with continuous trait variation. The IPM theory (Easterling et al. 2000; Ellner & Rees 2006) is used to understand how complex demographic processes, and the associated individual variation, affect population growth and the evolution of life history strategies (Jongejans et al. 2008). Until now, such models have been mostly applied to plant and mammalian populations (e.g., Ramula et al. 2009; Ozgul et al. 2010).

Most statistical population models are static and stochastic (Sanz & Bravo de la Parra 2007). They are divided into two classes: birth–death models and population growth models. On the one hand, birth–death models are a special case of continuous time Markov models and describe changes in a population through births and deaths, assuming that only one event happens at a time (Otto & Day 2007). On the other hand, population growth models consider two sources of stochasticity simultaneously: demographic and environmental (Sanz et al. 2003; Otto & Day 2007). In small populations, the effects of demographic stochasticity may be crucial, sometimes even causing total population extinction (Sanz et al. 2003; Sanz & Bravo de la Parra 2007).

A link between mathematical and statistical population models has been made recently using a state-space framework. Two processes are considered in state-space population models: one for state and one for observation (Fig. 2). The first process describes the state of a population at successive time steps, through demographic processes such as birth, survival and movement. The observation process links the unknown states to data on the population, recorded during surveys or experiments, and gives the probability of obtaining a particular observation depending on the population’s state (Thomas et al. 2005; Buckland et al. 2007; Patterson et al. 2008) (for a mathematical description of state-space models, see Buckland et al. 2004). To our knowledge, no state-space models have been applied to brown trout, although they have been successfully applied to Atlantic salmon (Salmo salar) by Rivot et al. (2004).

Figure 2.

 General structure of a state-space model. The yt are data observed given the true, but unobserved, state xt. Horizontal arrows depict the state process estimation of the true state of the population through time t, while vertical arrows depict the observation process. State-space models are a way to link mathematical and statistical population dynamics models.

Population genetics models

Classical population genetics models rely on the Wright–Fisher assumption of an idealised population, in which the entire population reproduces simultaneously and no selection occurs (Otto & Day 2007) (e.g., the infinite alleles and the stepwise mutation models of Kimura & Crow 1964 and Ohta & Kimura 1973, respectively; the island and the stepping-stone dispersal models of Wright 1943 and Kimura & Weiss 1964, respectively). The coalescent model (Kingman 1982; reviewed by Hudson 1990; Nordborg 2001) is a natural extension of classical (forward-time) population genetics models, and it investigates the shared genealogical history of genes (Rosenberg & Nordborg 2002; DeSalle & Amato 2004). Forward-time simulation programs (e.g., EasyPOP: Balloux 2001; SFS-CODE: Hernandez 2008) and coalescent, or backward-time, simulation programs (e.g., SEQ-GEN: Rambaut & Grassly 1997; MS: Hudson 2002) provide a means to explore the effects of micro-evolutionary processes on populations of constant size. The forward-time approach is more appropriate for studying how the long-term behaviour of evolutionary systems depends on initial conditions (Rosenberg & Nordborg 2002). The coalescent approach can be used as a simulation tool for (i) studying the effects of past evolutionary forces on current genetic variation, and thus to estimate parameters like migration rates and effective population sizes, and (ii) hypothesis testing and exploratory data analyses (Rosenberg & Nordborg 2002; Kuhner 2009).

The effects of the micro-evolutionary forces on populations can be studied through multiple analyses involving population genetics models (e.g., studies of Gomez-Uchida et al. 2008 on the Arctic charr (Salvelinus alpinus), and Whiteley et al. 2010 on the cutthroat trout (Oncorhynchus clarkii), both using the simulator EasyPOP). Here, we consider three main analyses. First, the genetic structure of a fish population can be investigated by using F-statistics (Wright 1969) to analyse historical patterns of population subdivision (Hartl & Clark 1989). Among the three inbreeding coefficients corresponding to the F-statistics, the FST coefficient detects changes in differentiation among populations and, hence, quantifies the degree to which populations are subdivided (Allendorf & Luikart 2007). Several studies on brown trout found moderate-to-strong genetic differentiation among Atlantic populations (global multilocus FST values from 0.03 to 0.60), both at the scale of river systems (Carlsson et al. 1999; Hansen et al. 2002; Lehtonen et al. 2009) and at the scale of basins (Estoup et al. 1998; Campos et al. 2006; Vilas et al. 2010), and strong differentiation among Mediterranean populations (FST values around 0.60; Krieg & Guyomard 1983; Apostolidis et al. 1996, 2008).

Second, FST can also be used as a basis for estimating past gene flow, i.e., the number of migrants exchanged among populations per generation (Holderegger et al. 2006), because gene flow is naturally related to population subdivision. Indeed, a lack of genetic exchange among populations is expected to result in genetic differentiation (Allendorf & Luikart 2007). Classification methods (also called assignment tests) address the following question: ‘Which population does a particular individual originate from?’ (see the review of Manel et al. 2005), while clustering methods allow to determine how many distinct genetic populations are present in a system (Pearse & Crandall 2004). Methods that permit both classification and clustering of individuals are the most frequently used (e.g., STRUCTURE: Pritchard et al. 2000; see Sonstebo et al. 2007a for an example of application to brown trout populations). Less sophisticated methods like multilocus contingency tests (as implemented in GENEPOP: Raymond & Rousset 1995; Rousset 2008) are a good alternative to clustering methods (Waples & Gaggiotti 2006). In addition to assigning individuals to their population of origin, classification methods can also be used to infer first-generation migrants (e.g., BAYESASS: Wilson & Rannala 2003). An alternative approach for studying current gene flow is the parentage analysis (reviewed by Wilson & Ferguson 2002; Jones et al. 2010; Jones & Wang 2010a). Using sibship and parentage analysis software (e.g., COLONY: Jones & Wang 2010b; Wang 2004), Hudy et al. (2010) were able to describe the spatial distribution of brook trout (Salvelinus fontinalis) spawning sites, and the dispersal from these sites after fry emergence (see Serbezov et al. 2010a for an example of application to brown trout). Broquet & Petit (2009) have recently reviewed the use of molecular genetic markers to estimate dispersal parameters.

Third, changes in genetic diversity and thus in population size can be inferred via the estimation of the effective population size, or Ne (Fisher 1930; Wright 1931). This is the size of an ‘ideal population’ that would have the same rate of genetic change owing to drift as does the population under consideration (Schwartz et al. 2007; Luikart et al. 2010). More roughly, Ne is an approximation of the number of breeding individuals producing offspring that live to reproductive age, and it enables direct tests for changes in population size by quantifying it (Schwartz et al. 2007). We focus on genetic methods that can be used to estimate contemporary or current Ne. Methods to estimate historical Ne have recently been reviewed in Charlesworth (2009). Recent coalescent programs are presented in Kuhner (2009); they estimate the product of effective population size times mutation rate (i.e., the effective number of migrants per generation) (e.g., MIGRATE: Beerli & Felsenstein 2001; Beerli 2006; LAMARC: Kuhner 2006). Quantification methods of contemporary Ne can be divided according to the number of samples they require; the lower the intensity of sampling, the higher the number of assumptions (Broquet & Petit 2009; Luikart et al. 2010). Examples of methods requiring a single sample from the same population are the heterozygote excess in progeny method (e.g., Pudovkin et al. 1996; Luikart & England 1999), and the linkage disequilibrium method (e.g., LDNe: Waples & Do 2008; ONeSAMP: Tallmon et al. 2008). They are less reliable compared to the temporal method, which is based on samples from the same population from at least two time periods (Schwartz et al. 2007). Several approaches of the temporal method have been proposed, based on moment (e.g., TempoFs: Jorde & Ryman 2007), likelihood (e.g., MLNE: Wang 2001; Wang & Whitlock 2003) or coalescent Bayesian (e.g., TM3: Berthier et al. 2002) estimators (Leberg 2005; Luikart et al. 2010). For instance, Ostergaard et al. (2003) as well as Jensen et al. (2005a) used a likelihood-based implementation of the temporal method on brown trout populations in Denmark (see also Table 6). All Ne quantification methods presented so far assume that samples come from a single isolated population, and thus concern related individuals, except for the MLNE program that was designed to jointly estimate Ne and migration rate using multiple samples from multiple generations of two or more populations (Vitalis & Couvet 2001; Skalski 2007).

Many fitness-related phenotypic characters (growth rate, age and size at maturity, etc.) are complex and quantitative in nature. They vary continuously and are coded by many interacting genes, which are in turn influenced in their expression by the environment (Naish & Hard 2008). Population genetics methods presented earlier are no longer suitable for this kind of traits, so complementary genetic methods have been developed. In quantitative genetics, the aim is to understand distributions of quantitative characters, and the temporal change of the means and variances of these distributions (Coulson et al. 2010). The infinitesimal model (Falconer & Mackay 1996; Lynch & Walsh 1998) describes the genetic basis variation of quantitative traits within populations and assumes that phenotypic differences observed among individuals are related to differences in a large number of genes, each of them having a minor effect (Wilson et al. 2010). Application of the infinitesimal model in natural populations is addressed by linear mixed effect models, and more specifically by the so-called animal model. This model is used to decompose the phenotypic variance of a trait into genetic and environmental sources of variance, to estimate parameters such as the heritability of this trait and genetic correlation with other traits (Wilson et al. 2010). We refer the reader to Kruuk (2004) and Wilson et al. (2010) for a general introduction to the animal model, and to Serbezov et al. (2010b) for an example of application of this model to brown trout populations. In the field of management and conservation of fish populations, the role of quantitative genetic methods has been discussed by Naish & Hard (2008), considering evolutionary effects of both fishing and adaptation to climate change issues. Quantitative genetics has also allowed the integration of ecology and genetics. For instance, the approach described in Coulson et al. (2010) is based on integral projection models (IPMs, see the previous section on Population dynamics models) and is aimed to explain within and between species patterns in quantitative characters, life history and population dynamics.

Population distribution models

The second modelling category corresponds to population distribution models. Most of them are descriptive (or phenomenological); they concentrate on observed patterns in the data and give a quantitative summary of the observed relationship among a set of measured variables (Ellner & Guckenheimer 2006; Bolker 2008). At the stream scale, habitat characteristics (e.g., water depth and velocity, substrate size) and preferences of stream fish are evaluated using habitat–hydraulic models (e.g., Ahmadi Nedushan et al. 2006; Anderson et al. 2006). At a larger scale, the relationship between fish occurrence and catchment characteristics (e.g., rainfall, elevation, vegetative cover) is addressed by spatial distribution models (e.g., Olden & Jackson 2002; Ahmadi Nedushan et al. 2006).

Population distribution models are thus a means to explain fish habitat preferences and distribution. But they can also be used to address questions relevant to the management and conservation, when integrated with population dynamics or population genetics models. That is the subject of the last section of this literature synthesis, which addresses spatial dynamics models and landscape genetics models and demonstrates how these can be used to predict the effects of anthropogenic impacts such as habitat alteration or barriers to gene flow on stream fish populations.

Habitat–hydraulic models

Habitat–hydraulic models are a combination of hydraulic models and habitat suitability models (Harby et al. 2004; Mouton et al. 2007b). Hydraulic models are intended to calculate water levels from characteristics such as water velocity, channel depth and channel width and can be one-, two- or three-dimensional (Harby et al. 2004; Clark et al. 2008). Habitat suitability models are used to evaluate the potential availability of fish habitat (Mouton et al. 2007b).

The evaluation of fish habitat suitability comprises three steps. First, the frequencies with which animals use various habitat types and the availability of these habitat types are observed (Railsback et al. 2003). Then, the ratio of habitat use to habitat availability is transformed into a measurement of habitat selection, the habitat suitability index. This index is based on curves representing the degree of preference displayed by fish over the complete range of different habitat variables found in a river, such as velocity, water depth and substrate size (Guay et al. 2000). Preference indices range from 0 (poor habitat) to 1 (best habitat), for each of the considered physical parameters. The fish preference curves of Bovee (1982) are the most used, but modified versions can be developed to adapt them to the stream under study. Finally, the aquatic space available to a fish species for a river at a given flow is quantified by the weighted usable area or WUA, an aggregate measurement expressed in square metres of habitat per 1000 m of river. The WUA is simply the sum of the products of the habitat suitability index by the wet section areas (Booker & Dunbar 2004; Clark et al. 2008). For instance, Ovidio et al. (2008) calculated the WUA for adult brown trout living in a Belgian tributary. Other examples are listed in Table 6.

Hydraulic and habitat suitability models were linked by Bovee (1982), through his IFIM. This concept is a means to describe habitat by including discharge variability, i.e., the change of stream flow in magnitude, frequency or duration at some point in time and space (Petts & Kennedy 2005). The methodology was first implemented in a suite of numerical models, called Physical HABitat SIMulation or PHABSIM (Waddle 2001). For example, such models were used by Nehring & Anderson (1993) to investigate the effect of flow related to habitat changes on wild rainbow trout (Oncorhynchus mykiss) and brown trout populations. Several other models based on PHABSIM were developed later, such as EVHA (Ginot 1995; Pouilly et al. 1995), and a new approach based on fuzzy set theory appeared recently (Ahmadi Nedushan et al. 2006) (see Table 6 for examples of application of these methods). The IFIM approach linking instream hydraulics to fish distribution is now being applied worldwide by environmental managers as part of environmental assessments and decision-making (Tharme 2003; Mouton et al. 2007b; Clark et al. 2008).

Spatial distribution models

The development of models aimed to predict the spatial distribution of organisms had two historic steps (Olden & Jackson 2002; Joy & Death 2004). Initially, only comparative studies were made describing linear and nonlinear relationships between environmental variables and the occurrence of populations or species. The next step was to predict their spatial distribution from these environmental factors by incorporating geographic information systems (GIS).

At the start, linear relationships between environmental variables (e.g., water depth) and fish occurrence (e.g., presence/absence data) were assumed. Traditional linear approaches include linear regression, multiple regression and discriminant analysis (see Table 6 for application examples). Because patterns of fish occurrence often exhibit complex (i.e., nonlinear) relationships to habitat heterogeneity and biotic interactions, alternative approaches were developed in the late 1990s. In such nonlinear models, occurrence is recorded for all sites or a random subset of sites in the study area (Buckland & Elston 1993). Classification and regression trees and artificial neural networks are typical examples of nonlinear techniques. The capacity of predictive models using GIS to fill in the gaps between sample sites permits to enhance the accuracy of spatial maps of the probability of fish occurrence (Joy & Death 2004; Dauwalter & Rahel 2008).

Spatial distribution models are widely used in the field of management and conservation of fish populations (Guisan & Zimmermann 2000; Olden & Jackson 2002). For instance, a general linear model was used by Schager et al. (2007) to analyse the influence of selected abiotic and biotic parameters on the density of brown trout populations (see Table 6 for other examples). A large number of competing approaches are available today. Therefore, comprehensive studies that compare the performance of these approaches (e.g., Manel et al. 1999; Olden & Jackson 2002) should be taken into consideration when using this kind of model.

Integration of population ecology and population distribution models – Addressing key questions for stream fish management and conservation

Here, we review two recently developed types of ecological models: spatial dynamics models and landscape genetics models. These models are attempts to integrate either population dynamics models or population genetics models across spatial scales; indeed, they combine population dynamics with stream scale habitat characteristics, and population genetics with landscape ecology, respectively. These two spatially explicit approaches make intensive use of individual-based simulation methods, for instance to incorporate demographic stochasticity (Marjoram & Tavaré 2006; Epperson et al. 2010; Segelbacher et al. 2010), so we first introduce individual-based simulation models. We review spatial dynamics models and landscape genetics models in the two next sections, with the intent to show how these models can address two key questions relevant to the management and conservation of freshwater fish, with a focus on the brown trout (Fig. 3). For each question, we give examples of individual-based simulation models that have been or could be applied to brown trout, and we present the data they require. In the final section, we address the developing field of demogenetics that employs individual-based simulation models.

Figure 3.

 Current combinations of ecological models to answer two key questions about the management and conservation of freshwater fish. At the stream scale, population dynamics models and habitat–hydraulic models are integrated to obtain spatial dynamics models. At the landscape scale, population genetics models and spatial distribution models are integrated to obtain landscape genetics models.

Individual-based simulation models

Occasionally used in ecology since the 1960s, individual-based models (IBM) made a breakthrough in the late 1980s when object-oriented programming (OOP) became available (Grimm 1999; DeAngelis & Mooij 2005; Breckling et al. 2006). Since then, a number of applications have arisen in ecology: reproduction, dispersal, formation of patterns among individuals, foraging and bioenergetics, species interactions, local competition and community dynamics, population management strategies (Breckling et al. 2005; DeAngelis & Mooij 2005). Individual-based modelling techniques can be viewed as complex computer simulations, which the goal is to mimic a real-world empirical system by creating a ‘computer-world’ to represent the system’s biological processes (Peck 2004). The approach lends itself directly to the use of OOP methods: elements of the program are ‘objects’ which pass, receive and respond to ‘messages’ (Kimmerer et al. 2001). Thus, OOP is a natural way for implementing IBMs and is designed to be independent of software platforms.

Individual-based simulation techniques treat individuals as unique and autonomous discrete entities, and these models can be used to explore how the properties of higher level ecological entities, like populations, emerge from interactions of individuals with each other and their environment (Grimm & Railsback 2005). The great potential of IBMs comes at a cost. First, demographic stochasticity is an intrinsic property of any IBM (DeAngelis & Mooij 2005), and it is thus not possible to study the effects of its absence on a population. Second, data requirements can be a serious limitation. Indeed, IBMs require much data to be calibrated and validated (Jorgensen 2008), with more complex models requiring more parameters to specify the processes (Grimm & Railsback 2005; Breckling et al. 2006). This structural complexity can make IBMs harder to implement, analyse and communicate than are equation-based models such as population dynamics models and F-statistics. Furthermore, the high number of parameters requires detailed biological knowledge (Breckling et al. 2006), e.g., behavioural data that can be difficult to obtain for some species. Thus, limits in biological knowledge might restrict the application of this model type (Breckling 2002).

The respective limitations and strengths of both equation-based and individual-based models should be considered when choosing the appropriate approach for an investigation (Breckling et al. 2006). Individual-based models should be used when a modeller wants to consider one or more of the following individual-level characteristics to explain system-level behaviour: heterogeneity among individuals, local interactions and adaptive behaviour which is based on decision-making (Thiele & Grimm 2010).

Individual-based simulation models should not be confused with ecological models dealing in some way with individuals. For instance, in population genetics, classification and clustering methods are implemented in individual-centred programs, i.e., for which the main focus of the analysis is on individuals (Excoffier & Heckel 2006).

Spatial dynamics models – How can we predict fish population responses to management intervention?

Stream fish populations are subject to natural control processes that continually affect their structure and abundance, as well as their life cycles, in response to a wide range of factors (Milner et al. 2003). The knowledge of how fish populations are naturally regulated is thus necessary to determine how they might respond to a management intervention, for instance a modification of the stream flow resulting in an alteration of their habitat. The first step is to model the dynamics of the studied population. Then, we need to add habitat heterogeneity in the model and, therefore, consider small spatial scales. By modifying variables in the model, we can finally predict the outcome of the management intervention. The use of population dynamics models in a spatially explicit framework (i.e., spatial dynamics models) is one of the possible answers to address the first key question: ‘How can we predict fish population responses to management intervention?’ This question is management-oriented and focuses on a single population at a small spatial scale (i.e., the stream reach).

Our review of habitat–hydraulic models demonstrates that understanding the spatial variability of stream habitat and the complex interactions between habitat and fish is a major issue in stream ecology. Brown trout show strong preferences for habitat features such as spawning substratum, temperature, flow and water quality (see Elliott 1994). The inclusion of stream scale habitat characteristics into population dynamics models has lead to the development of spatial dynamics models, which can theoretically be linked to the ecohydraulics field. Ecohydraulics is an interdisciplinary approach that tends to understand the demographic processes that limit population density, to determine which life stages are important and to determine whether, and how, demographic rates are affected by flow (Lancaster & Downes 2010).

The first attempts to link habitat–hydraulic models with biological mechanisms were made using the bioenergetic approach (Hayes et al. 2000; Guensch et al. 2001). Bioenergetic models explain individual growth and habitat choice by quantifying the balance between energy gained through feeding, and energy lost through swimming, digestion, food capture, growth, reproduction, urine and faeces (Fausch 1984; Rosenfeld 2003; Booker et al. 2004). They have been used for drift-feeding salmonids to predict habitat selection (e.g., Hughes 1998; Elliott & Hurley 1999), habitat suitability (e.g., Braaten et al. 1997) and long-term population growth (e.g., Railsback & Rose 1999; Hayes et al. 2000). More sophisticated attempts have been made through the development of physically- based (e.g., Booker et al. 2004; Hayes et al. 2007) and individual-based bioenergetic models. For example, the individual-based model of Van Winkle et al. (1998), developed to evaluate behavioural responses of brown trout populations to physical habitat changes, has been substantially revised and implemented in the inSTREAM’s software (Railsback et al. 2009).

Another way to include biological characteristics into habitat–hydraulic models is to combine models dedicated to the evaluation of the physical habitat of fish, such as habitat suitability models and habitat–hydraulic models, with population dynamics models. First, matrix projection models were enhanced to include environmental stochasticity (Tuljapurkar 1990). For instance, the fish survival rates in a Leslie matrix (see the section on Population dynamics models) can be reduced according to fluctuation in the habitat using WUA time series (see Capra et al. 2003 for an example with brown trout). Most recently, Letcher et al. (2007) studied the importance of dispersal and fragmentation on the dynamics of a brook trout population, using a spatially explicit stage-based matrix model. Second, the development of integral projection model formulations for stochastic environments is another promising approach (see Rees & Ellner 2009). Two examples applied to aquatic insects that illustrate the integration between ecology and hydraulics are described in Lancaster & Downes (2010). Third, spatial dynamics models implemented with individual-based simulation techniques permit the integration of not only habitat heterogeneity but also the complex interactions between individuals and their habitat (DeAngelis & Mooij 2005). For instance, the software HexSim (Schumaker 2011) enables the simulation of terrestrial populations’ complex life history under multiple spatial themes representing habitat, disturbance regimes or even landscape barriers.

As a result of the merging of population dynamics and habitat–hydraulic models, spatial dynamics models need three types of data: demographic, hydraulic and habitat suitability. First, demographic and reproductive data on stream fish are gathered by observations, trapping and electrofishing. When fish are marked in some way (e.g., fin clipping), statistical methods known as ‘capture–recapture methods’ can be used for estimating the size of the studied population as well as fish survival rates. Reproductive observations such as number of eggs, number of nests and nest success are estimated by monitoring gravid females and nests counting. Second, hydraulic data are based on transect sampling of water depths and flow velocities and on visual estimation of substrate classes (Mouton et al. 2007b). Third, preference curves needed to evaluate habitat suitability of fish (see the section on Habitat–hydraulic models) are produced from observational studies of habitat utilisation, literature surveys and expert opinion (Heggenes et al. 2002).

Landscape genetics models — How is the genetic structure of fish populations influenced by landscape characteristics?

To manage natural fish populations living in a river basin, it is vital to identify both the number of populations to manage and the interactions that exist among them. These two points allow information to be gathered on the genetic structure of populations, which is a prerequisite to study the effects of landscape features on the genetic processes regulating those populations. The first step is to infer the current and historical genetic structure of a population to determine the number of genetically distinct fish populations present in a system and the interactions between them. Then, we need to integrate the riverscape (i.e., the riverine landscape, see Wiens 2002) in the model. The use of population genetics models in a spatially explicit framework (i.e., landscape genetics models) is one answer to address the second key question: ‘How is the genetic structure of fish populations influenced by landscape characteristics?’ This question is conservation-oriented and considers several fish populations at a larger spatial scale.

In the field of population genetics, coalescent simulation programs have been extended to incorporate the influence of environmental parameters on migration (e.g., SPLATCHE: Currat et al. 2004). On the side of quantitative genetics, quantiNEMO (Neuenschwander et al. 2008a) is an individual-based program that investigates the effects of mutation, selection, recombination and drift on quantitative traits in populations connected by migration and located in a heterogeneous habitat. The inclusion of spatial details into genetics models allowed the development of landscape genetics models, theoretically linked to the discipline of the same name. Landscape genetics aims to study the interactions between landscape features and micro-evolutionary processes (i.e., within species) that generate genetic structure across space (Manel et al. 2003), and it has been identified as a field that integrates population genetics, landscape ecology and spatial statistics (Storfer et al. 2007).

The key steps of landscape genetics are twofold (Manel et al. 2003; Pearse & Crandall 2004). First, the spatial detection and location of genetic discontinuities among populations allow the determination of spatial genetic patterns (Manel et al. 2003). Common patterns described in the salmonid literature are isolation by distance (i.e., genetic differentiation among populations that increases with their geographical distance, see Poissant et al. 2005; Dionne et al. 2008), barriers to gene flow (e.g., impassable waterfalls or dams, see Dillane et al. 2008), mosaic structure of evolutionary lineages (see Sanz et al. 2002; McKeown et al. 2010) and coexistence of anadromous and resident migration morphs (i.e., sympatric populations, see Ferguson 2004; Narum et al. 2008). The second key step is the correlation of spatial genetic patterns with environmental features, such as elevation, rainfall and upstream distance, using statistical methods similar to those presented in the section on Spatial distribution models. Geographic information systems are then used to produce statistical and visual materials on landscape characteristics and patterns (Johnson & Gage 1997).

Statistical approaches and software used in landscape genetics have been reviewed (Excoffier & Heckel 2006; Storfer et al. 2007; Guillot et al. 2009), and comparisons of different approaches have been made (Hauser et al. 2006; Latch et al. 2006; Chen et al. 2007; Balkenhol et al. 2009b). Research on landscape genetics initially focused on the terrestrial landscape (e.g., Bruggeman et al. 2010), but the field has expanded to include marine and aquatic systems (Sork & Waits 2010). In seascape genetics, a framework integrating biological–physical oceanographic and genetic models has been recently described by Galindo et al. (2010). In riverscape genetics, computer programs implementing statistical methods more adapted to freshwater fish have been developed. They assume that individuals can only disperse through stream corridors and no longer through a two-dimensional landscape (e.g., BARRIER: Manni et al. 2004; STREAM TREES: Kalinowski et al. 2008). Another example is the extension of the spatially explicit coalescent simulation program SPLATCHE to linear habitats (AQUASPLATCHE: Neuenschwander 2006), which was used to study the colonisation history of the Swiss Rhine basin by bullhead (Cottus gobio) (Neuenschwander et al. 2008b). Other riverscape genetics studies investigated the influence of the spatial structure of river networks on population connectivity (e.g., Labonne et al. 2008; Morrissey & de Kerckhove 2009), and identified barriers to gene flow for fish (see Storfer et al. 2010). Several partial attempts that have been made to apply the theory of landscape genetics to brown trout populations are presented in Table 6.

The flexibility of individual-based simulation models has progressively increased the incorporation of spatial and ecological details and processes into landscape genetics models (Balkenhol et al. 2009a; Epperson et al. 2010). For instance, an individual-based spatially explicit landscape genetics model, CDPOP, has recently been developed by Landguth & Cushman (2010) to simulate dispersal, mating and genetic exchange as probabilistic functions of cost distance among individuals (Segelbacher et al. 2010). An alternative approach proposed by Epperson et al. (2010) is to combine individual-based programs from ecology and genetics like HexSim and simuPOP (Peng & Kimmel 2005; Peng & Amos 2008), but this has not been tested yet. The scope of the landscape genetics field is expanding and will tend to be more interdisciplinary as it merges with geography, ecology, evolution and phylogeography (Epperson et al. 2010; Sork & Waits 2010).

In landscape genetics models, genetic data are combined with spatial data. First, genetic data, i.e., individual multi-locus genotypes and allele frequencies of fish samples, are provided by DNA markers (e.g., micro-satellites, see Carvalho & Hauser 1998). DNA is extracted from fin or muscle biopsies, which are collected from fish captured either by trapping or by electrofishing. Second, spatial data are generally presented in the form of digital maps from GIS (Jager et al. 2005). For stream fish, such data usually provide information about watershed characteristics. Guidance on sampling and analysis of landscape genetics data can be found in Anderson et al. (2010).

The developing field of individual-based demogenetics

The population dynamics and population genetics models reviewed earlier show that considerable advances have been achieved in both fields, mostly in separate ways. However, ecological and life history characteristics such as population size, dispersal pattern and mating system have been shown to influence fish population genetic divergence through their effects on genetic drift and gene flow (Turner & Trexler 1998; Dawson et al. 2002; Whiteley et al. 2004). Individual-based simulation techniques enable the joint generation and analysis of demographic and genetic data (Palsboll et al. 2007) and have thus allowed the development of attempts including either more genetic realism into population dynamics models or more biological realism into population genetics models. Table 7 lists the software associated with these attempts, which are described below, and the software mentioned earlier in the section on Population ecology models. This table shows (i) the great flexibility of coalescent simulation techniques, which is somewhat limited by their difficulties in integrating natural selection in the simulations, and (ii) the progressive use of individual-based simulation techniques.

Table 7.   List of software from population dynamics and population genetics relevant to individual-based demogenetic models. Features implemented in each program are specified.
Software (Reference)FieldIBMVarPopSelRecMigrMut
  1. *In the new version of the MS program (released October 14, 2007), the user can specify if the population has been growing or shrinking exponentially.

  2. IBM, individual-based model; VarPop, variable population size; Sel, natural selection; Rec, recombination; Migr, migration or dispersal; Mut, mutation; PD, population dynamics; PG, population genetics, with forward-time (F) or coalescent (C) simulation programs; QG, quantitative genetics.

MODYPOP (Sabaton et al. 1997; Gouraud et al. 2001)PDNoYesNoNoYesNo
EasyPOP (Balloux 2001)PG-FNoNoNoYesYesYes
SFS-CODE (Hernandez 2008)PG-FNoNoYesYesYesYes
SEQ-GEN (Rambaut & Grassly 1997)PG-CNoNoNoNoNoYes
MS (Hudson 2002)PG-CNoNo*NoYesYesNo
MIGRATE (Beerli & Felsenstein 2001; Beerli 2006)PG-CNoYesNoNoYesNo
LAMARC (Kuhner 2006)PG-CNoYesNoYesYesNo
METASIM (Strand 2002)PD + PGYesYesYesNoYesYes
VORTEX (Lacy 2000)PD + PGYesYesNoYesYesYes
simuPOP (Peng & Kimmel 2005; Peng & Amos 2008)PG-FYesYesYesYesYesYes
NEMO (Guillaume & Rougemont 2006)PG-FYesYesYesYesYesYes
Eco-genetic model (e.g., Wang & Hook 2009)QGYesYesYesNoNoNo

In the field of population dynamics, the individual-based model METASIM (Strand 2002) provides a flexible environment, based on matrix projection theory, to simulate population genetics of complex population dynamics. Population dynamics models are also frequently used for population viability analysis or PVA, which aims to predict the likelihood of the persistence of an endangered species for a given time in the future (DeSalle & Amato 2004). PVA programs such as VORTEX (Lacy 2000) aim to study the effects of deterministic forces and stochastic events on the dynamics of populations, including changes in genetic variation.

In population genetics, earlier coalescent simulation programs assumed a constant population size, because the original formulation of the coalescent approach was made under the assumption of a Wright–Fisher model. More realistic versions of this approach have been developed to take into account factors such as genetic recombination, gene conversion, population subdivision, population growth and demography (Marjoram & Tavaré 2006; Kuhner 2009). A method named approximate Bayesian computation (Beaumont et al. 2002; reviewed by Bertorelle et al. 2010 and Csilléry et al. 2010) proposed a more flexible framework to address complex scenarios (Segelbacher et al. 2010). In addition, forward-time simulation programs such as simuPOP and NEMO (Guillaume & Rougemont 2006) explicitly model the properties of individuals and specify arbitrary patterns of population size changes. In the field of quantitative genetics, the recent development of individual-based eco-genetic models allowed evaluating the relative importance of genetic and ecological effects on fish life-history traits and stock productivity by taking into account quantitative genetic traits inheritance (e.g., Dunlop et al. 2009; Wang & Hook 2009).

In all of these attempts to develop models that integrate demography and population genetic structure, the expected level of population genetic divergence is still estimated under specific population size change and dispersal rate patterns (Palsboll et al. 2007). A further understanding of how demography influences the genetic structure of stream fish populations is an important next step in developing comprehensive individual-based demogenetic models.

Discussion and summary: towards a spatially explicit demogenetic model for brown trout

Our review demonstrates the historic development of ecological models for brown trout over the past 30 years, and the extent to which modelling currently plays a role in the management and conservation of freshwater fish. To our knowledge, this is the most comprehensive effort to date to review and synthesise this broad topic. We found that initially the development of ecological models followed four separate trajectories: population dynamics, population genetics, habitat preferences and spatial distribution. Our review highlights efforts that have been made to integrate ecological models across spatial scales and shows the increasing use of individual-based simulation techniques. First, we addressed models integrating stream scale habitat characteristics into population dynamics (i.e., spatial dynamics models), or landscape ecology into population genetics (i.e., landscape genetics models). Table 8 lists the programs associated with these models and mentioned earlier. Second, there have been efforts to integrate population dynamics and population genetics models, and we reviewed individual-based demogenetic models that have appeared recently (second half of Table 7). Comprehensive and flexible individual-based demogenetic models however are in the early stages of development. Furthermore, we believe there is a need to integrate spatially explicit methodology into demogenetic models, to better address the important issue of ecological scale (e.g., Wiens 1989; Peterson & Parker 1998; Schneider 2001; Lischke et al. 2007).

Table 8.   List of software from spatial dynamics and landscape genetics relevant to individual-based, spatially explicit demogenetic models. Features implemented in each program are specified.
Software (Reference)FieldIBMVarPopSelRecMigrMut
  1. *In the new version of the program, SPLATCHE 2 (Ray et al. 2010), a recombination model has been implemented.

  2. IBM, individual-based model; VarPop, variable population size; Sel, natural selection; Rec, recombination; Migr, migration or dispersal; Mut, mutation; PG, population genetics, with forward-time (F) or coalescent (C) simulation programs; QG, quantitative genetics; SD, spatial dynamics; LG, landscape genetics.

SPLATCHE (Currat et al. 2004)PG-CNoYesNoNo*YesYes
BARRIER (Manni et al. 2004)LGNoNoYesNoYesNo
STREAM TREES (Kalinowski et al. 2008)LGNoNoNoNoYesYes
AQUASPLATCHE (Neuenschwander 2006)PG-CNoYesNoNoYesYes
inSTREAM (Railsback et al. 2009)SDYesYesNoNoYesNo
HexSim (Schumaker 2011)SDYesYesNoNoYesNo
quantiNEMO (Neuenschwander et al. 2008a)QGYesYesYesYesYesYes
CDPOP (Landguth & Cushman 2010)LGYesYesNoYesYesYes
KERNELPOP (Strand & Niehaus 2007)SD + LGYesYesYesNoYesYes

Future model development efforts should include spatially explicit demogenetic models that integrate demographic, genetic and spatial data. The interplay between environmental processes and demogenetic characteristics is crucial. For instance, at the stream scale, an environmental perturbation can represent a radical and rapid change in the demographic and genetic structure of a population, producing local catastrophes and reductions in suitable areas where fish can thrive (Pertoldi & Topping 2004). At a larger scale, the structure and complexity of the riverscape can affect the demogenetics of fish populations by influencing the occurrence of their dispersal strategies, and by interacting with the micro-evolutionary processes. Individual-based, spatially explicit demogenetic models may be particularly useful for fish and wildlife management and conservation questions. They can serve as a tool for understanding specific questions like the influence of barriers on the dispersal of a species, or as a simulation tool for testing hypotheses concerning the response of a population to future changes in the environment, either natural or anthropogenic.

To our knowledge, only one spatially explicit individual-based demogenetic model has been developed. KERNELPOP (Strand & Niehaus 2007) provides a population genetics simulation environment with both demographic and spatial realism. According to the authors, this model allows the implementation of almost any arbitrary population demographic and genetic model in a spatially explicit context (Table 8). Although R (R Development Core Team 2010) was used as an interface for this model, the simulation engine was implemented in C++. Therefore, the ease with which new features can be added to the model may be limited, because the user has to learn the complex details of a standard programming language (i.e., C++ in this case). A flexible yet simple method to realistically link demography and population genetics at various spatial scales is needed.

NetLogo (Wilenski 1999) provides a simplified programming language, a graphical interface and an automated simulation experiment manager that allows the user to build, observe and run IBMs. Furthermore, it is now possible to call R from NetLogo (Thiele & Grimm 2010). We believe that the combination of these powerful tools may provide a simple and flexible framework to implement individual-based, spatially explicit demogenetic models, which can in turn become a major tool in management and conservation of freshwater fish populations.

In summary, the ecological models applied to brown trout have become increasingly complex, and they have been used to predict an expanding range of responses at various spatial and temporal scales. Most recently, efforts have focused on linking the models across these scales and disciplines, but much remains to be done. Although a few models combining both demographic and genetic characteristics of populations have appeared recently, no comprehensive, spatially explicit, demogenetic model has been proposed. We have described the framework for such a model, advocating the use of individual-based simulation techniques to include individuals’ variability and different levels of spatial details. This framework, which involves both NetLogo (for model programming) and R (for model development, testing and understanding), will be tested and applied to stream-dwelling brown trout populations to describe the variations in their demogenetic structure at both stream and watershed scales. We anticipate that the application of such spatially explicit, individual-based demogenetic models to fish and wildlife populations will further improve their management and conservation, by generating testable hypotheses as to how populations might respond to natural or anthropogenic disturbances.


The first author is a recipient of a Ph.D. grant financed by the ‘Fonds pour la formation à la Recherche dans l’Industrie et dans l’Agriculture’ (F.R.I.A.). We thank Volker Grimm, Emmanuel Hanert, Filip Volckaert, Michael Morrissey and one anonymous reviewer for useful comments on earlier versions of the manuscript.