# IBASAM: a spatially explicit demo-genetic individual-based model for anadromous salmonids (meta)populations

Amaïa Lamarins, François de Coligny, Jacques Labonne, Etienne Prévost, Cyril Piou, Craig R. Primmer, Mathieu Buoro

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Amaïa Lamarins, François de Coligny, Jacques Labonne, Etienne Prévost, Cyril Piou, et al.. IBASAM: a spatially explicit demo-genetic individual-based model for anadromous salmonids (meta)populations. Individual-based Ecology, 2025, 1, pp.e153305. 10.3897/ibe.1.153305. hal-05296592

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# IBASAM: a spatially explicit demo-genetic individual-based model for anadromous salmonids (meta)populations

Amaïa Lamarins<fnref n="1" />,<fnref n="2" />,<fnref n="3" />✉, François de Coligny<fnref n="4" />✉, Jacques Labonne<fnref n="2" />,<fnref n="3" />✉, Etienne Prévost<fnref n="2" />,<fnref n="3" />, Cyril Piou<fnref n="5" />✉, Craig R. Primmer<fnref n="1" />✉, Mathieu Buoro<fnref n="2" />,<fnref n="3" />✉

## Abstract

To improve our understanding and management of species' response to environmental and anthropogenic changes, there is a critical need for integrative modeling approaches that encompass demographic, ecological and evolutionary processes in a unified framework. This is especially crucial for anadromous salmonid fishes that hold significant socio-economic value. Mechanistic models, particularly Demo-Genetic Agent-Based Models (DG-ABMs), are promising tools to address this challenge. Building on previous work, we present IBASAM, an Individual Based Anadromous Salmonids Model. It incorporates significant advancements, including new processes such as dispersal and in-river movement, enhanced flexibility for simulating diverse spatial configurations- from river dendritic networks to metapopulations and multiple marine areas- and the integration of variation in life history phenology and the genetic architecture of traits. Additionally, a user-friendly interface has been developed to facilitate broader application. This improved versatility enables IBASAM application to a number of populations and species of anadromous salmonids, making it well-suited for a wide range of ecological and evolutionary studies. The model is particularly suited for investigating (meta)population eco-evolutionary dynamics, persistence, and adaptation to environmental changes, as well as exploring management options including fisheries regulation and river connectivity restoration. We summarize the model's structure and illustrate some of its novelties, parameterization, and emerging patterns with the case study of a naturally reproducing Atlantic salmon population simulated over 100 years, with constant fishing rates and no climatic trend. Our simulations successfully reproduced 30 variables observed over 30 years in the Scorff population. IBASAM opens up new opportunities for research on the response of salmonid meta-populations to environmental changes, the exploration of management practices, with further developments possible.

**Key words:** Demo-Genetic Agent-Based Model, Anadromous salmonids, Life history strategies, Evolution, Genetic architecture, Phenotypic plasticity, Dispersal, Metapopulation, Management

OPEN ACCESS

Academic editor: Steven Railsback

Received: 18 March 2025

Accepted: 20 May 2025

Published: 13 June 2025

Citation: Lamarins A, de Coligny

F, Labonne J, Prévost E, Piou C, Primmer CR, Buoro M (2025)

IBASAM: a spatially explicit demo-genetic individual-based model for anadromous salmonids (meta) populations. Individual-based Ecology 1: e153305. https://doi.org/10.3897/ibe.1.153305

Copyright: © Amaïa Lamarins et al.
This is an open access article distributed under
terms of the Creative Commons Attribution
License (Attribution 4.0 International – CC BY 4.0).

<footnote n="1">1 Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland</footnote>

<footnote n="2">2 Universite de Pau et des Pays de l'Adour, INRAE, ECOBIOP, Saint-Pée-sur-Nivelle, France</footnote>

<footnote n="3">3 Management of Diadromous Fish in their Environment, OFB, INRAE, Universite de Pau et des Pays de l'Adour, Institut-Agro, Saint-Pée-sur-Nivelle, France</footnote>

<footnote n="4">4 AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France</footnote>

<footnote n="5">5 CIRAD, UMR CBGP, Montpellier, France</footnote>

<footnote>Corresponding author: Mathieu Buoro (mathieu.buoro@inrae.fr)</footnote>
Introduction

Species are encountering rapid environmental changes and multiple anthro-
pogenic pressures that threaten their persistence. Assessing their ability to
respond necessitates the integration of eco-evolutionary processes and their
underlying mechanisms into prospective models, alongside environmental dy-
namics. Phenotypic plasticity, genetic adaptation of phenotypic and phenolog-
ical traits, and dispersal are the fundamental components of species' adap-
tive capacity (Beever et al. 2016; Thurman et al. 2022), enabling populations
persistence and resilience to environmental changes. This highlights the need
for modeling approaches that span scales, from genetic mechanisms at the
individual level to dynamics at the metapopulation scale (Baguette et al. 2017;
Romero-Mujalli et al. 2019; Thorogood et al. 2023). Ultimately, such compre-
hensive models provide valuable insights into the interplay of eco-evolutionary
processes and environmental changes, enhancing our ability to manage spe-
cies responses to future challenges (Seaborn et al. 2023).

Demo-Genetic (or Eco-Genetic) Agent-Based Models (DG-ABMs) offer prom-
ising avenues to tackle these challenges (Xuereb et al. 2021). These mecha-
nistic models are well-suited for evaluating species' persistence because they
explicitly (i) include eco-evolutionary processes and their underlying mecha-
nisms, and (ii) account for inter-individual variation and interactions that are
key drivers of selection. Importantly, DG-ABMs do not rely on predefined fit-
ness functions; instead, fitness and population-level properties emerge from
individual decisions, interactions, and feedbacks, shaping genetic diversity and
population dynamics (Lamarins et al. 2022b). Various generic DG-ABM plat-
forms exist for simulating (meta)populations, such as NEMO (Guillaume and
Rougemont 2006), SLIM (Messer 2013), or CDMetaPOP (Landguth et al. 2017).
While flexible (e.g., allowing addition of life history traits or life cycle events),
these generic models may not be suitable for species with complex life cycles.

Anadromous salmonid fishes, which migrate between freshwater and marine
habitats, exhibit complex life cycles and diverse life history strategies (Erkina-
ro et al. 2019; Persson et al. 2023). Hence, their response to threats such as
migration barriers (Lindley et al. 2006), climate change (Crozier et al. 2021),
and overfishing (Christensen et al. 2003), are challenging to predict. Beyond
their ecological significance, numerous anadromous salmonid species have
substantial socio-economic value due to their cultural heritage and their impor-
tance in both recreational and commercial fisheries across the globe (Woo and
Noakes 2014; Kochalski et al. 2022). Additionally, they are emblematic mod-
el species for studying eco-evolutionary dynamics (e.g., Hendry and Stearns
2004; Primmer 2011), and evidence of dispersal also suggests metapopulation
dynamics (Keefer and Caudill 2014; Lamarins et al. 2024) despite their strong
philopatric behavior. Given the extensive knowledge of salmonid population
dynamics, from genetic architecture to demographic processes, a comprehen-
sive simulation tool is critically needed to integrate this information holistically.
Such a tool would enhance our understanding of their future trajectories under
environmental and anthropogenic pressures while enabling the evaluation of
management and conservation strategies.

IBASAM (Individual Based Atlantic Salmon Model, Piou and Prévost 2012)
and its extension MetalIBASAM (Lamarins et al. 2022a), were developed to
assess the effect of stressors on evolutionary demography of a single popula-
tion or a set of interconnected Atlantic salmon populations respectively. How-
ever, they have key limitations. First, they are not fully spatially explicit and are
limited to the river scale, preventing the simulation of finer-scale structures (e.g.,
upstream / downstream gradient, estuaries, dendritic networks) and within-riv-
er movements influenced by river connectivity. Also, MetalBASAM simulates
metapopulations by running several IBASAM models in parallel with individuals
exchanged between them, which limits the addition of dispersal mechanisms
requiring explicit inter-population feedbacks such as density-dependent immi-
gration (Berdahl et al. 2016). Additionally, it assumes a single marine domain,
despite evidence of multiple migration and growth areas with varying condi-
tions (Rikardsen et al. 2021). Second, IBASAM's temporal scale limits its ability
to capture variation in the phenology of life histories, despite growing evidence
of phenological shifts in response to environmental changes (Kovach et al.
2013; Woods et al. 2022). Third, previous versions lack the capacity to simulate
complex genetic architectures, relying on a polygenic framework without lo-
cus-specific or dominance effects. Yet, advances in genomics highlight the role
of complex genetic architectures of traits and major-effect loci, such as sex-de-
pendent dominance in salmon age at maturity (Barson et al. 2015), which have
implications for genetic diversity, evolutionary potential, and conservation in
salmonids (Waples et al. 2022). Simulating such architectures of adaptive traits
could advance our understanding of species' adaptive capacities (Xuereb et
al. 2021). Lastly, IBASAM currently lacks an end-user graphical interface, limit-
ing accessibility for a wider scientific audience, management applications and
stakeholder engagement (McInerny et al. 2014; Gaydos et al. 2021).

Here, we present a major revision of IBASAM (now standing for Individual
Based Anadromous SALmonids Model) addressing previous limitations and
providing enhanced flexibility in various aspects of salmonid life cycle represen-
tation, within a fully spatially explicit framework. Key advances include flexible
spatial scaling (from river sections with variable connectivity to metapopula-
tions and multiple marine areas), improved phenology, complex genetic archi-
tectures of traits, and a user-friendly interface. We provide a summarized de-
scription of the model, with additional details available in the Suppl. material 1.
To demonstrate its capabilities, we simulate ~ 30 years of past and 70 years
of future evolution of a virtual population mimicking a comprehensively moni-
tored population of Atlantic salmon in France, assuming constant fishing rates
and no climatic trends. However, the model is adaptable to other anadromous
salmonid species and river systems. Finally, we discuss the model's new fea-
tures and highlight opportunities for further development.

Model description: summary ODD

IBASAM is implemented in the Capsis simulation platform (https://capsis.cirad.fr, Dufour-Kowalski et al. 2012), offering a user-friendly interface (Suppl. material 1: note 1). The Capsis platform can also be launched from **R**, enabling users to run large-scale simulations and perform more advanced statistical analyses. The model executable file, Java source codes, R scripts to create initialization files and launch the model, as well as a comprehensive user guide are all available at https://doi.org/10.5281/zenodo.14825431. A complete, detailed model
description, following the ODD (Overview, Design concepts, Details) protocol for describing individual- and agent-based models (Grimm et al. 2006, 2020) is provided in Suppl. material 1: note 2. Here, we provide an overview of the model features (Fig. 1 and Suppl. material 1: fig. S9).

The overall *purpose* of IBASAM is to investigate how selective fishing, environmental changes (e.g., variation in water temperature, discharge, and growth conditions), and management actions (e.g., spatially defined fishing, conservation measures, and connectivity restoration by dam removal) influence the eco-evolutionary dynamics of salmonid populations. This version of IBASAM enables the exploration of two key aspects: (1) the potential effects of metapopulation dynamics through dispersal on the evolution of life history traits, population and individual-level data.

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Visualization of IBASAM following the vODD format (Szangolies et al. 2024). At initialization, fish individuals with their state variables are generated in river sections (reaches, separated by weirs and eventually grouped by populations) and sea regions. Several submodels (circled numbers) are run on a daily basis following the salmon life cycle. Population and individual metrics can then be derived from a simulation run (e.g., 50 years) according to several scenarios.</figcaption></figure>
lation stability, persistence, and resilience, and (2) the influence of complex ge-
netic architectures underlying life history traits. To consider our model realistic
enough for its purpose, one can use patterns of population dynamics, including
changes in demographic, phenotypic and genotypic (e.g., allele frequencies)
patterns over time. Here, we evaluate the model's ability to reproduce a variety
of patterns using observed time-series of abundance, size/mass/date distribu-
tions at different ages/life stages, and life history strategies (i.e., proportion of
early maturation, years at sea) of a long-term monitored population (see Fig. 2).

The model includes the following *entities*: individuals are fish, redds (spawn-
ing nests) and fry siblings; and spatial units (river, population, reach, weir and
sea region; Fig. 1). The *state variables* characterizing these entities are listed in
Suppl. material 1: note 4, table S1. Fish belong to a population (a gene pool) and
have a status (parr, smolt, anadromous), unique values of body size, weight, fat
reserve, dates of life history events (e.g., migration, reproduction) and a genetic
material coding for both genotypic and phenotypic values of heritable traits
(neutral trait, growth potential, philopatry propensity, migration and matura-
tion thresholds). Redds are created after reproduction and record the genetic
material of the mother and potentially multiple fathers, along with the number
and weight of the fertilized eggs. To limit computational time and because off-
spring suffer high mortality during the first weeks after emergence, redds pro-
duce new super-individuals called fry siblings, separated by sex, representing
the total number of offspring issuing from the fertilized eggs of two parents
before being individualized as fish. Each fry sibling is characterized by attri-
butes similar to fish with an additional number of siblings. Regarding the *spatial*
and *temporal* resolution and extent, this version of IBASAM is spatially explicit,
capable of simulating complex watersheds comprising multiple rivers and/or
interconnected basins, as well as several marine regions. Rivers can be further
subdivided into single or multiple reaches, separated by weirs with user-defined
passability (connectivity). By default, a river is a population, but it can contain
several populations (groups of reaches). Reaches and sea regions are the spa-
tial units where processes unfold (Fig. 1). Each reach and sea region is defined
by distinct characteristics, including habitat area, environmental conditions
(e.g., water temperature and growth conditions), and fishing intensity. These
conditions may vary between spatial units but are assumed to be uniform with-
in each unit. The basic time step for all individual processes is daily, with some
processes occurring only within a time window (i.e., maturation, migration and
reproduction processes). Simulation can be extended over multiple years to
generate successive generations and monitor long-term dynamics (e.g. ~ 20
generations over 50 years for a southern European Atlantic salmon population).

The most important *processes* of the model, which are repeated every *time step*, are ten submodels representing critical life events and processes of salmonid life cycle: 1) reproduction and redd (nest) creation, 2) emergence from the redd, 3) genetic coding and transmission, 4) growth, 5) survival, 6) seaward migration decision, 7) sexual maturation, 8) migration (including inter-basin dispersal), 9) movement (downstream/upstream, including intra-basin dispersal) and 10) fishing (Fig. 1). Processes are applied daily, tailored to the location (river or sea, population), individual status (e.g., juvenile, anadromous, or mature), and temporal factors (e.g., season, defined time windows). The simulation begins on an initial date specified by the user. The model follows a predefined
sequence of processes, starting with the river phase and then the marine phase (Box 1). A detailed explanation of the underlying processes and mechanisms is presented in Suppl. material 1: note 2.

## Box 1. Model processes sequence.

### River phase: reproduction, growth, survival, and life history decisions

In the river, reproduction occurs within a reproduction time window, during which females create redds. Reproduction events incorporate size-dependent fecundity and a mating system reflecting sexual selection and salmonid behavior. Egg survival to emergence (first feeding) depends on water temperature, river flow, and juvenile density. At emergence, redds form groups of fry siblings, with certain genetic traits (e.g., growth potential and maturation thresholds) transmitted through a bi-allelic multilocus system. Each group inherits alleles from both parents, with phenotypic expression influenced by heritability and environmental variability. Fry siblings undergo three main processes: survival, growth, and early sexual maturation decision. They grow in weight influenced by genetically determined growth potential, water temperature, population density, and river flow. Weight increments are then allocated to either fat reserves or somatic growth (increase in body size). Early maturation decisions occur within a time window and are determined by fat reserves exceeding genetically defined sex-dependent maturation thresholds. At the end of summer, fry siblings are individualized into fish with the "parr" status. Other genetic traits, such as migration thresholds and philopatry propensity, are inherited through the same bi-allelic multilocus mechanism. Then, individual fish undergo daily processes of aging, growth, survival, and sexual maturation, as described earlier. Survival probabilities vary by life stage, seasons and with sexual maturation decision. Additionally, survival is influenced by a growth-survival trade-off, where rapid growth tends to reduce survival. Fish may then decide to migrate to the sea during a decision time window. Similar to maturation, the seaward migration decision is governed by a comparison between somatic growth and a genetically determined sex-dependent threshold. Individuals exceeding this threshold become "smolts" and migrate to the estuary the following spring, while others remain in the river for an additional year to accumulate sufficient energy reserves.

### Marine phase: growth, survival, and reproductive conditioning

Upon entering the sea, post-smolts are transferred to the first sea region and become "anadromous". Fish growth depends on genetic growth potential and oceanic conditions. Growth increments are allocated to fat reserves or somatic growth, mirroring river-phase allocation. Survival at sea depends on body size and fishing rates. Fish may decide to mature the following year (in which case they spend only one winter at sea, OSW) based on their growth during their first summer at sea and their sex-dependent maturation threshold. Fish that do not accumulate enough reserves to surpass their maturation threshold will remain at sea for another year and may migrate to a different sea region. These fish are then classified as Multi Sea Winter (MSW). Once the maturation decision is made, anadromous fish begin their return to the river for reproduction. If multiple rivers are available, some fish may disperse to non-natal rivers, influenced by their genetic philopatric propensity (inter-basin dispersal). Fish return to the estuary of the selected river, where they remain for one day before beginning their movement toward the spawning sites. Their movement can be altered or prevented by weirs of varying passability, and they may also be subject to fishing mortality. During their upstream movement, fish may further disperse to non-natal populations depending on their philopatric propensity (intra-basin dispersal).

The most important *design concepts* of the model are emergence, adaptation, and observation. All individual life history traits (growth, survival, maturation, migration, reproduction) emerge from a mechanistic framework that integrates environmental variables, interactions with other individuals, demographic dynamics and genetic structures, themselves emerging from individual life histories via an eco-evolutionary feedback (Lamarins et al. 2022b). Indeed, maturation, migration, philopatry and growth potential are adaptive traits which influence individual fitness. They result from the interplay of a genetic basis (e.g. via thresholds) and environmental conditions (e.g. via environmental cue) through adaptive phenotypic plasticity. All individual traits or summary metrics (e.g. number of individuals, median size) can be recorded at specific time steps (e.g., day 91 and 274 on the calendar of each year) or at life-cycle events (e.g. during seaward / riverward migration, reproduction) defined at initialization by the user.
The model is *initialized* with a spatial structure (numbers and locations of rivers, reaches, populations, weirs, and sea regions), environmental conditions and fishing rates in river reaches and sea regions, river weirs passability rates, genetic architecture of traits, and virtual populations with individual fish at every individual life stage (parr, smolt, anadromous). Redds and fry siblings are not created at initialization. Model dynamics are driven by population parameters values (Suppl. material 1: table S3), spatial structure and genetic architecture of traits. *Input data* consist of daily times' series of reach (water temperature, water flow, fishing rates), weir (passability rates) and sea region (growth condition, fishing rates) conditions (Suppl. material 1: note 5).

Key processes that were added in the model are the transmission of her-
itable traits with flexible genetic architecture, phenology variation, dispersal
and in-river movement. Individual traits such as maturation and migration
thresholds, growth potential and philopatry propensity are genetically herita-
ble, combining quantitative genetics with Mendelian inheritance (Suppl. ma-
terial 1: fig. S4). Trait phenotypic expression results from the additive effects
of genetic and environmental components, with genotypic values controlled
by multiple independent bi-allelic loci. While the model excludes mutation
(given the low number of generations) and inter-locus interactions (epista-
sis), it allows for variation in locus effect sizes and intra-locus interactions
(i.e. dominance effects). Some traits exhibit sex-, life stage-, or location-de-
pendent expression. This update aligns with recent advances in understand-
ing the genetic architecture of maturation in male parr (Lepais et al. 2017;
Debes et al. 2021) and anadromous salmon (Barson et al. 2015; Sinclair-Wa-
ters et al. 2020). Maturation threshold now has a unique genetic basis, but
its expression varies by sex and life stage (parr vs anadromous), allowing
for the implementation of sex-dependent dominance patterns. Considering
recent work on the sex-specific migration propensity of brown trout (Kane et
al. 2022; Lavender et al. 2024), a similar framework has been applied to the
expression of seaward migration decision. The migration threshold also has
a unique genotypic basis with sex-specific phenotypic expression. Similarly,
the river and sea growth potentials have a unique genotypic basis, but their
phenotypic expression varies between river and sea. This update differs from
previous IBASAM versions, which treated traits independently by sex and life
stage. The genetic architecture can also differ from one population to anoth-
er (named as gene pools), either by only adjusting allele frequencies or by
modifying the entire genetic map (allele effects, dominance, etc.). A neutral
trait is also genetically heritable but does not influence any model processes
- it serves to track genetic drift. Additionally, this version includes variation in
life history phenology, such as migration timing, which depends on the selec-
tion of time windows and parameter values (e.g. migration thresholds). Final-
ly, dispersal between rivers basins, along with in-river movements processes
and intra-basin dispersal, are key additions to the model. Dispersal decision
is driven by individual philopatric propensity - which is here genetically deter-
mined - and the selection of the destination river is based on distance and
attractiveness following a dispersal kernel. In-river movements are simulated
for both smolts downstream migration and anadromous upstream migration,
using implicit or explicit methods based on habitat attractiveness (altitude,
size, sex ratio) and weir passability trials.
# Illustration with a population of Atlantic salmon

## A. Case study

We illustrate IBASAM using an Atlantic salmon population of the Scorff River in Brittany, France. This population has been the focus of a comprehensive long-term monitoring program, now spanning 28 years. Here we simulate the Scorff population at 25% of its carrying capacity and using a spatial structure of 22 reaches with various habitat areas (including estuary, Suppl. material 1: fig. S1) separated by 15 weirs with various passability rates. We also simulate two distinct sea regions, representing the environmental conditions likely encountered by one-sea-winter (OSW) and multi-sea-winter (MSW) individuals. We simulated river and sea environmental conditions under the assumption of no long-term trend in future conditions (neutral scenario; see Suppl. material 1: note 6 for details). The same time series of rivers, weirs and seas conditions were used across all simulations. Consequently, variation among simulations arises solely from variation in the initial population and demographic stochasticity within the modeled processes.

To simplify our model illustration, we deactivated some processes and/or simplified others. First, although unrealistic based on field observations, we simulated a single population with dispersal deactivated, ensuring all fish returned to their natal population (the Scorff River). Second, we directly transferred smolts from their natal reach to the estuary before their transition to the sea (default in-river movement method). Similarly, anadromous fish were transferred from the estuary directly to one of the river's reaches. Finally, we used a simple genetic architecture of traits. The number of bi-allelic loci was set arbitrarily at 20 to limit the risk of rapid genetic drift and random fixation of alleles in the population. All loci are unlinked and have the same effect size with allele co-dominance. At initialization, the individual genotypes are generated by drawing alleles from a multinomial distribution considering allele frequencies of 0.5 at each locus and for each trait. The heritability of the maturation and migration thresholds were set to 1 because we assume that environmental variance is already present in the liability trait (fat and size, respectively) according to the conceptual framework of the Environmental Threshold Model (Tomkins and Hazel 2007).

## B. Model parameterization

As in Piou and Prévost (2012), numerous model parameters values can be set according to the extensive literature on salmonids, such as those related to egg production and juvenile growth (Suppl. material 1: note 4). In addition, various parameters values are likely to be population-dependent and can be informed based on the knowledge of the simulated system from long-term monitoring programs. To parameterize the model to the Scorff population, we first used the observations and knowledge of the system such as for time windows of life history events (reproduction, migration) or weight-length relationships parameters (Suppl. material 1: table S3). As far as possible, we did the same for theoretical assumptions. For example, parameter values governing the growth-survival trade-off were set to reflect a trade-off in the river, where higher growth rates reduce survival, while this trade-off was deactivated for sea growth for parsimony.

However, some model parameters remain uninformed because they cannot be easily estimated or because they are theoretical quantities. In that case, they
can be calibrated in the pattern-oriented framework (Grimm et al. 1996; Grimm and Railsback 2012), i.e. by comparing the output from the individual-based model simulations to several patterns observed from natural populations. We opted for a hybrid method combining an Approximate Bayesian Computation (ABC, Beaumont 2010) approach and trial-and-error adjustments for parameters that are difficult to estimate using ABC alone (i.e., no updating of the prior distributions). We implemented the ABC approach using the R package *abc* (Csilléry et al. 2012) with the rejection method to estimate the posterior distributions of 14 parameters (survival rates, river growth, thresholds; Suppl. material 1: fig. S10). 80 000 combinations of parameters values were drawn from uniform distributions using a Latin hypercube sampling design. We ran all combinations for a virtual population over 32 years, from 1990 to 2022 (allowing a 5-year burn-in before the first observed data). 32 summary statistics (averaged over the years; see Suppl. material 1: note 6) were used to compare to observed data.

We thus checked that, with the final parameterization combining informed
parameter values and estimates from the ABC approach, the model simulations
reproduced the range of values and inter-annual variation of 30 time series of
observed data (Fig. 2). We first compared demographic outputs of model sim-
ulations to time series of abundance of different life stages (parr, smolts, anad-
romous adults) estimated from a hierarchical Bayesian model based on data
collected by ORE DiaPFC (Buoro et al. 2019). We also compared distributions
of size, weight, and dates of life history events, as well as male and maturing
proportions of different life stages from the model simulations to observations
in the Scorff river. A yearly electrofishing survey of juveniles provided informa-
tion about size, weight and maturing status of parr (0+, 1+ and more), while two
trapping systems provided information about size, weight and migration date
of migrating smolts (1+, 2+ and more) and returning adults (OSW, MSW, iter-
oparous). Overall, the distribution of demographic, phenotypic and phenological
metrics over 50 simulation replicates with the selected parameterization are
very similar to field observation patterns (Fig. 2) despite a few discrepancies.

C. Projections and emerging correlation patterns

To illustrate IBASAM's capacity to simulate eco-evolutionary dynamics, we ran
the model for 100 years and 50 simulation replicates, using the same starting
conditions as in the parameterization simulations. With the river and sea con-
ditions simulated here (constant fishing and no trend in climate conditions),
the model led to stable demographic, phenotypic, and genotypic structures of
population over years (Fig. 3) as expected under stabilizing selection. The num-
ber of adults returning from the sea, their mean sea age, migration, maturation
thresholds and allele frequency are typical examples of stationary distributions
in mean and variance across simulation replicates, except for the variance of
allele frequency of the first locus of maturation threshold (Fig. 3F). A slight
decrease in the growth potential of the 0+ parr can, however, be noticed. Note
that maturation thresholds are lower for males compared to females, leading to
lower age at first maturity at sea in males compared to females.

Correlations among phenotypic traits may arise from the genetic architecture
set for simulations, but also emerge dynamically through model processes and
vary among life stages. As expected, we measured an overall correlation of 0.11
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Times series of simulation results (distribution over 50 replicates, orange) with the retained parameters values (Suppl. material 1: table S3) versus observations from the monitoring of the Scorff river (distribution over observations, black) per life stage and metrics. Metrics include the number of individuals (A; simulation results scaled up to the real population size), the body size (B) and weight (C), the proportion of mature individuals (D1–D2) and the proportion of males (D3–D6), the proportion of iteroparous individuals (D7), and the date of life history events (E). Distributions show the median (point) and inter-quantile intervals (50% and 95%, lines). Horizontal dashed black lines represent the assumed date of emergence (E1) and number of parr 1 + (A2), based on expert knowledge.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Times series over 100 years of simulation results with the retained parameter values (Suppl. material 1: table S3). Distribution over 50 replicates (median and 95% quantiles), by year, of the number of returning adults to the river (A), mean sea age of returning adults (B; by sex), phenotypic growth potential of 0+ parr at the end of summer (C), migration threshold of 1+ smolts at seaward migration (D; by sex), maturation threshold of returning adults (E; by sex), and allele frequency of locus 1 of maturation threshold of returning adults (F; by sex).</figcaption></figure>

between the phenotypic values of river and sea growth potentials (Suppl. material 1: table S15), reflecting their shared genetic basis. Similarly, we found a correlation of 1 between the precocious and anadromous maturation thresholds in OSW male individuals (Suppl. material 1: table S15). Interestingly, we also detected a correlation of approximately 0.3 between the phenotypic river growth potential of 1+ smolts and their migration threshold in both sexes (Suppl. material 1: table S14), suggesting that individuals with high migration thresholds need robust growth to overpass them. In contrast, there was no correlation between the migration threshold and the precocious maturation threshold in 1+ male smolts.

Although loci are unlinked, patterns of linkage disequilibrium (LD) – allele covariation among loci – can still emerge from model simulations. For a given trait, θ, the difference between genetic variance (σ²_G, variance of individual genotypic values) and genic variance (σ²_A = Σ'n_Loci' 2p_jq_jσ²) divided by the genic variance, quantifies the covariance between loci (Gavrilets and Hasting 1995; Le Corre and Kremer 2003). According to the Bulmer effect, selection is expect-
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Distribution of $\theta$ over 50 simulation replicates (mean and 50% quantiles), by year and sex, for the neutral trait among OSW individuals (A), the growth potential (B) and migration threshold among 1+ smolts (C), and the maturation threshold among OSW individuals (D).</figcaption></figure>

ed to induce negative covariance between loci under selection (Bulmer 1971; Walsh and Lynch 2018). Although no LD was measured for the neutral trait (Fig. 4A) and LD was absent at initialization, our simulations revealed a negative disequilibrium ($\sigma_G^2 < \sigma_A^2$) for growth potential, maturation, and migration thresholds (Fig. 4), suggesting that these traits are under selection. Furthermore, results showed no sex differences in the LD pattern for growth potential and migration thresholds among smolts. However, sex differences in LD pattern suggested stronger selection on maturation threshold in adult females (average $\theta$ = -0.46) than in males (average $\theta$ = -0.15, Fig. 4D).

## D. Sensitivity analysis

Finally, we carried out a sensitivity analysis of the model to 30 weakly informed parameters using the variance decomposition method (Sobol 1990), a highly recommended approach for models of this type (Ligmann-Zielinska et al. 2020). We used this method to decompose the variance of 8 summary statistics (averaged over the last 5 years of simulations, Suppl. material 1: fig. S11) into components contributed by each parameter. We ran the model over 25 years with the same conditions as for the prediction simulations with 80 000 sets of parameters values following a Sobol design. We computed the Sobol index of first order for each parameter using the R package sensitivity (Iooss et al. 2024). We chose not to evaluate the sensitivity of parameters that were informed, or already evaluated
in Piou and Prévost (2012) and for which the process or value has not been changed in this new version of IBASAM. The range of values tested (Suppl. material 1: table S16) was defined based on a realistic range around the values of the parameterization that would not lead to population extinction or burst.

The results of the sensitivity analysis indicate that the model outcomes are sensitive to parameters related to growth and growth-survival trade-off. Two parameters of the growth-survival trade-off in river (*sigRIV* and *maxRIV*) explained most of the variance of the model outcomes according to the Sobol index (Suppl. material 1: table S16). The mean value of the genetic growth potential in river ($\mu_{pG}$) and at sea ($\mu_{pGsea}$) set at initialization, and the summer growth activity (*activity*<sub>summer</sub>) are also important parameters. However, this result strongly depends on the range of values tested for each parameter as well as the model outcomes considered. Specific model outcomes may be particularly influenced by some parameters that won't influence others. For example, the proportion of iteroparous (repeat spawning) individuals among MSW is strongly related to the post-reproduction anadromous mortality rates (*ReproMortalityFemale* and *ReproMortalityMale*; Suppl. material 1: fig. S11), and the number of smolt 1+ is largely influenced by the mean value of the migration thresholds set at initialization ($\mu_{pLmidF}$ and $\mu_{pLmidM}$; Suppl. material 1: fig. S11). Note that, according to Piou and Prévost (2012), the model was also sensitive to the river growth rate (*dr*) and the mean capacity to store fat (*pPercFm*).

## Discussion

IBASAM (now standing for Individual Based Anadromous Salmonids Model) introduces several major advancements compared to the previous release as outlined in Suppl. material 1: table S17, including the implementation of key processes such as dispersal and in-river movement. The model offers enhanced flexibility to simulate a wide range of spatial configurations, from dendritic river networks to metapopulations and multiple marine areas. IBASAM also incorporates variation in life history traits, phenology, and underlying genetic architectures. These additions significantly expand the model's spatial and temporal realism, without increasing computational cost or sacrificing generality. This enhanced versatility facilitates the adaptation of IBASAM to diverse populations and species of anadromous salmonids, making it well-suited for a broad spectrum of ecological and evolutionary studies. To support wider adoption, a user-friendly interface has been developed and the model's integration within the Capsis platform facilitates code and library sharing among a community of modelers, fostering collaboration and iterative improvements. We therefore strongly recommend that users adopt this version of IBASAM.

We used the example of a French Atlantic salmon population to demonstrate the ability of IBASAM to successfully reproduce 30 demographic and phenotypic patterns of field observations over approximately 30 years. Under a neutral scenario (i.e., no climatic trends or changing anthropogenic pressures), we highlighted emerging properties at demographic, phenotypic, and genotypic levels, including sex differences in sea age (or age at maturity) and in the intensity of selection on the maturation threshold. The emerging correlations among traits or loci (linkage disequilibrium) may be particularly relevant, as they could influence the population's response to environmental or anthropogenic selective pressures. However, the simulations presented here are inherently influenced by simplifying
assumptions (e.g., no emigration/immigration, no climate change). Consequent-
ly, our findings should not be interpreted as exact representations of the natu-
ral population's dynamics taken as a reference but rather as an illustration of
IBASAM's potential applications. This example serves as a foundation for further
applied or hypothesis-driven studies under realistic environmental scenarios.

E. Model innovations enhancing species' adaptive responses

These key enhancements in IBASAM – particularly the introduction of new her-
itable traits - broaden the model’s capacity to capture adaptive responses and
expand the range of possible evolutionary trajectories in anadromous salmonid
populations. First, seaward migration decision is now under genetic control. In
contrast to the preceding version (Piou and Prévost 2012), where anadromy was
defined by a fixed reaction norm, and solely sea age at maturation (i.e. the mat-
uration threshold) could evolve, the updated model allows anadromy to evolve
in response to selective pressures, such as exploitation. These changes also
lead to the emergence of new trait correlations, such as between smolts growth
potential and migration threshold. In addition, dispersal among and within river
basins during riverward migration is now an individual process incorporating
a genetic basis. Dispersal propensity might thus constitute another adaptive
response of salmonids populations to spatially heterogeneous selective pres-
sures, such as variable exploitation intensity. Consequently, the model can now
generate potentially divergent population responses compared to those simu-
lated using the MetalBASAM version (Lamarins et al. 2025). Finally, some traits -
such as the maturation threshold - now have a single genetic basis across sexes
and life stages, with phenotypic expression modulated by sex or life stage. This
genetic constraint contrasts with earlier IBASAM versions, where traits were
modeled independently for each sex and life stage. As a result, the updated
model may lead to different evolutionary trajectories under selection, potentially
diverging from previous findings (Piou and Prévost 2013; Piou et al. 2015).

F. Model parameterization and sensitivity

The numerous changes applied to several model processes required the adjust-
ment of previous and new parameters. We used a hybrid method combining tri-
al-and-error adjustment with estimations based on the ABC approach. The ABC
approach is useful to find a set of values for parameters for which we know
little about. However, it is not easy to estimate a large number of parameters
with complex DG-ABMs because of the lack of diverse informative summary
statistics, computing time limits, or potential confusions between parameters.
Using this hybrid method, we obtained a set of parameter values suitable for
reproducing the demographic and phenotypic dynamics of Atlantic salmon in
the Scorff river based on long- term monitoring data. Nevertheless, it does not
exclude that other sets of parameters value would allow similar patterns.

The sensitivity analysis revealed that the model is overall particularly sen-
sitive to parameters related to growth and growth-survival trade-off, but more
specific model outcomes may be sensitive to other parameters. Although not
explicitly tested here, the model is also very sensitive to the time windows of
life history events provided at initialization (e.g., migration, maturation). For
example, depending on the position and duration of the evaluation time window for the decision of maturation, the proportion of maturing individuals will largely vary for a given maturation threshold because of the linear projection used in this process (Suppl. material 1: section G.7), especially if there is seasonal variation in growth conditions (e.g., river or sea growth conditions).

The model is also particularly sensitive to the within-river spatial configuration, that is, the number of sections. First, dividing the river in more reaches considerably reduces computing time. A 50-year simulation using this configuration (22 reaches) takes about 10 min (and 2Go of memory) to run whereas it takes about 45 min (same memory) to simulate the same river subdivided into 2 reaches only. This effect arises from the sexual selection process, which is confined to individuals within each section rather than the entire population. Second, the spatial configuration influences the density effect on survival at emergence, regulated by a stock-recruitment relationship. An adjustment is necessary because the carrying capacity estimated for the Scorff River (Prévost and Lebot 2022), suitable for simulating a few sections, was inadequate when the river was divided into 22 sections. This discrepancy occurs because the river's overall carrying capacity does not necessarily equate to the sum of individual sections capacities, particularly if sections are heterogeneous (Zhang et al. 2021). Third, the number of reaches can influence the number of reproductive events per individual (e.g., if sex ratio is biased in reaches). Finally, combined with the explicit process of in-river movement, the spatial configuration may influence the timing of seaward migration of smolts (with consequences on sea growth and anadromous size) and the within-river spatial distribution of returning anadromous individuals (especially the late ones). Hence, a set of suitable parameter values is specific to the spatial configuration of the river at stake.

## G. Perspectives of development and research opportunities

IBASAM offers new opportunities for research on the response of numerous salmonid fish species to environmental changes and the exploration of different management options. The open-source code available via the Capsis platform allows for specific adjustments and further developments. For instance, integrating a genetic basis for phenological traits that may be under selection, along with more realistic selection processes, could improve our understanding of salmonids' adaptive capacity to environmental changes. For example, variability in spawning timing could be explored under the alternative premise that it is a plastic (Jonsson and Jonsson 2009) or genetic (Stewart et al. 2002) response to changes in the temperature regime. The timing of emergence is also an important phenological trait which may have a genetic basis and may be under stabilizing selection (Einum and Fleming 2000; Carlson and Seamons 2008).

Incorporating greater realism into movement and migration processes could also improve our understanding and management of salmonids. In particular, accounting for the factors influencing migration timing is important, as variation in migration patterns reinforces portfolio effects and contributes to the stability of populations facing changing environments (Carr-Harris et al. 2018; Schindler 2019). Variation in seaward migration phenology may have a genetic basis (Stewart et al. 2006), be influenced by individual phenotypic status (Birnie-Gauvin et al. 2021), or by environmental conditions (Sykes et al. 2009). As fish
transition to the marine environment, spatial variation in growth and survival conditions shapes key life history traits, such as age at first maturation. Our flexible model simulates these dynamics across distinct sea regions, with potential to incorporate additional zones and growth patterns, thereby improving the realism of marine growth and survival processes (Utne and Mousing 2025). Modeling in-river fish movements also opens avenues for research on the consequences of individual variation in spawning or growth areas (Hendry et al. 2001; Bouchard et al. 2018) and the role of connectivity (e.g. dams influence) on migration timing, habitat accessibility, and overall population resilience.

Finally, investigating the implications of alternative genetic architectures underlying key life history traits, such as maturation, could yield valuable insights, particularly in the context of selective pressures like fishing (Kuparinen and Hutchings 2017). Leveraging recent advancements in salmonid genomics, the model enables more nuanced representation of genetic variation and inheritance patterns. Expanding this flexibility to include mechanisms such as epistasis and pleiotropy could further enrich the model. For example, the maturation locus *vgll3* has been associated with repeat spawning (iteroparity; Aykanat et al. 2019; Mobley et al. 2024), while growth potential could also be involved in seaward migration decision (Debes et al. 2020). These potential developments could significantly improve our ability to simulate and forecast population responses under different management, ecological, and evolutionary scenarios.

## Acknowledgements

The model was coded on the Capsis-4 modelling platform (http://capsis.cirad.fr) maintained by François de Coligny and Nicolas Beudez. We thank Miguel de Navascués for discussions on the ABC approach. We acknowledge CSC (IT Center for Sciences, Finland) for access to computational resources.

## Additional information

### Conflict of interest

The authors have declared that no competing interests exist.

### Ethical statement

No ethical statement was reported.

### Funding

Funded by the European Union (ERC, FishLEGs, 101054307). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. We also acknowledge funding from the Region Nouvelle Aquitaine, Communauté Agglomeration Pays Basque, UPPA, INRAE and the French Biodiversity Agency (OFB). This work was conducted within the International Associated Laboratory MacLife.

### Author contributions

Conceptualization: MB, AL, EP, CP, CRP; Methodology, Software: AL, MB, FC, JL; Validation, Formal analysis, Investigation: AL, MB; Writing – Original draft: AL, MB; Writing
- Review and Editing: AL, MB, EP, CRP, JL, FC, CP; Visualization: AL; Supervision, Project administration, Funding Acquisition: MB, CRP.

## Author ORCIDs

Amaïa Lamarins https://orcid.org/0000-0002-6639-9283

François de Coligny https://orcid.org/0000-0002-8538-3009

Jacques Labonne https://orcid.org/0000-0001-5953-3029

Cyril Piou https://orcid.org/0000-0002-9378-9404

Craig R. Primmer https://orcid.org/0000-0002-3687-8435

Mathieu Buoro https://orcid.org/0000-0001-7053-3767

## Use of AI

Artificial intelligence (DeepL Write, ChatGPT) was used to correct errors in grammar, spelling, and sentence construction.

## Data availability

The model executable file, Java source codes, R scripts to create initialization files, launch the model, analyze simulation results (including Shiny app), as well as a comprehensive user guide, are all available at https://doi.org/10.5281/zenodo.14825431.

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**Supplementary material 1**

**Supplementary notes 1–9**

Authors: Amaïa Lamarins, François de Coligny, Jacques Labonne, Etienne Prévost, Cyril Piou, Craig R. Primmer, Mathieu Buoro

Data type: pdf

Copyright notice: This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.

Link: https://doi.org/10.3897/ibe.1.153305.suppl1