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A simulation study of the determinants of adaptation of an invasive species

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Selection

Reproduction

Migration

Regulation

A simulation study of the determinants of

adaptation of an invasive species.

Source Sink Trait value Fitness Intr odu cti ons

Adaptation is considered as a key process determining the ability of introduced species to become invasive. There have been few simulation studies based on explicit genetic models of evolution to study the factors influencing adaptation after introduction [1]. Furthermore for the sake of simplicity and generality, analytical models of evolution are often limited to either sexual or asexual populations in a mutation/drift equilibrium. These models are not relevant to a large class of invasive species (e.g., fungal plant pathogens) because they exhibit rapid expansion dynamics (non-equilibrium) and are capable of both modes of reproduction.

We develop a demogenetic model of species introduction using an improved version of quantiNemo [2] : a given rate of asexuality can be set and a species can be either haploid or diploid. We simulate the following scenario: some individuals are introduced from a source environment to a new environment with a different adaptive landscape (Figure 1). A quantitative trait coded by few loci can evolve on this adaptive landscape thanks to mutation, selective pressure and genetic drift. Using this model, we show how the population expansion dynamic of the introduced population is affected by the rate of asexuality, mutation parameters and distance between source and sink optimum.

Figure 1 : Simulated scenario : A source population in

selection-mutation-drift equilibrium sends migrants into a sink population. Sink and source population have a different optimum. The immigrants (red rectangle) need to adapt to their new environment (red arrow).

Figure 2 : Life cycle events of individuals in quantiNemo.

Explanatory variable

Mutation rate Optimum

74.5 54 100 40.6 0.26

Invasion time lag 58.6 11.5 100 12.9 0.32

100 34.2 16.6 0.81

60.3 3.17 6.6 100 43.8 0.57

Invasion time lag 40.4 1.6 1.7 100 0.48 0.34

Genetic Variance in the source Variance of mutation effects Asexuality

rate Explained Variance

E xp la in ed v ar ia ble Invasion probability Genetic variance in the source Invasion probability 0 0.05 0.2 0.5 0.8 0.95 0.999 0.9999 1 0. 2 0. 4 0. 6 0. 8 1. 0

Probabilité d'émergence en fonction du taux d'asexualité Pr ob ab ili té d 'é m er ge nc e en fo nc tio n

Asexuality rate Asexuality rate

Asexuality rate Inva si on proba bi li ty Inva si on ti m e la g Ge ne ti c va ri anc e in the sourc e

Figure 4 : Effect of asexuality rate on invasion probability and time lag.

Figure 5 : Effect of

asexuality

rate on genetic variance in the source

population.

Differences of optimum between the source and the sink populations and mutation parameters (mutation rate and variance of mutation effect) are the main factors influencing the success of an invasion (Table 2). Nevertheless, asexuality rate has a substantial effect on invasion dynamic. Figure 4 shows that an optimum of invasion probability and time lag is reached for partially asexual species with a high rate of asexuality (~ 95% ). This can be explained by the influence of asexuality rate on genetic variance in the source population (Figure 5). We hypothesize that a high asexuality rate allows a source population to retain a higher proportion of mutations because of a less efficient background selection (Hill-Robertson effect). This increases the genetic variance in the source that can be transmitted to the sink through migration. Thus, evolutionary potential and invasive capacity of immigrants are improved under these reproductive conditions.

References :

[1] Holt, R. D., Gomulkiewicz, R. & Barfield, M. 2003. The phenomology of niche evolution via quantitive traits in a 'black-hole' sink. Proceedings of the Royal Society of London Series B-Biological Sciences 270: 215-224.

[2] Neuenschwander, S., Hospital, F., Guillaume, F. & Goudet, J. 2008. quantiNemo: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation. Bioinformatics 24: 1552-1553.

Table 1 : List of fixed and varying parameters of the model. The burn-in

phase is a time to allow the source population to reach mutation-genetic-drift equilibrium.

Bazin Eric, Mathé Hugo, Carlier Jean, Ravigné Virginie || Laboratoire BGPI, CIRAD, Département BIOS, Montpellier, France || eric.bazin@cirad.fr

0 100 200 300 400 500 0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0

0 Dynamique de la population puits,

taux de mutation = 0 génération n o m b r e d 'i n di v id u s

Time since first introduction

● Genetic variance in the

source ● Invasion probability Popul at ion si ze

Figure 3 : 5 simulation examples of the population dynamic in the sink and

measured characteristics.

Invasion time lag

Fixed parameters Value

Number of loci 10

1 Carrying capacity of source

Carrying capacity of sink

Burn-in phase 1000 gen

Simulation duration 500 gen Fecundity

Varying parameters Values

Environmental variance {0, 1} Mutation rate

Mutation effect variance {0.01, 0.05} {2.4, 2.8} Asexuality rate [0, 1] Migration rate Strength of selection (ω) 1000 ind 750 ind 5 ind.gen-1 {10-3, 10-4} locus-1.gen-1

Distance between optimum (D)

{0.001, 0.005, 0.01} ind.gen-1

W =e

− P−ZOpt 2 2 2 ZOpt1 ZOpt2 D

P

Table 2 : ANOVA results. Each line corresponds to a fitted linear model. The

last column gives the proportion of variance explained by the model and values in each cell give the F statistic standardised by a factor so that the highest F value is 100 after standardisation.

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