INTRODUCTION
Dispersal is a complex life history trait that influences demographic
and genetic processes, hence dispersal plays an important role in the
eco-evolutionary dynamics of geographically structured populations
(Legrand et al., 2017; Van Dyck & Baguette, 2005). Dispersal affects
evolutionary processes by leading to gene flow that increases genetic
variation within and affects the genetic structure of populations
(Holsinger & Weir, 2009). Variation in dispersal also impacts local
population sizes and densities, habitat use and (re)colonization in
fragmented populations, and, ultimately, these effects can influence the
viability and persistence of populations and species (Clobert, Baguette,
Benton, & Bullock, 2012; Saastamoinen, 2008). Because of its
fundamental importance, it is essential to understand whether and how
fast dispersal rates can evolve (Ronce, 2007; Saastamoinen et al.,
2018). Estimating the heritable genetic component and examining the
genetic architecture of dispersal is needed to understand causes of
variation in the dispersal phenotype and for predicting its adaptive
evolutionary potential (Orr, 2005; Zera & Brisson, 2012).
Dispersal-related traits (such as wing shape, locomotion performance or
speed) have previously been shown to be heritable in birds and insects
with an average heritability (h2 ) of 0.35
(Saastamoinen et al., 2018). Another meta-analysis revealed that the
average heritability of movement behavior over 15 different studies
(including both dispersal and migration) was found to be 0.46
(Dochtermann, Schwab, Anderson Berdal, Dalos, & Royauté, 2019).
However, estimating the heritability of dispersal or dispersal syndromes
(i.e. traits associated with dispersal) is a challenging task due to the
complexity of the dispersal event itself. Dispersal propensity may be
affected not only in one or more of the dispersal stages (departure,
transfer and settlement) but also by dispersal-related phenotypic traits
and their interactions with the environment (Bowler & Benton, 2005;
Ronce, 2007; Saastamoinen et al., 2018). Due to the need for accurate
identification of dispersers and resident individuals, which relies on
the quality and extent of mark-recapture data over sufficiently large
geographic areas to cover normal dispersal distances, estimating
heritability of dispersal and dispersal related traits is challenging,
but such estimates have been obtained in birds and insects more often
than any other taxa (Brown, Phillips, & Shine, 2014; McGaugh, Schwanz,
Bowden, Gonzalez, & Janzen, 2010; Saastamoinen et al., 2018; Waser &
Jones, 1989; Zera & Brisson, 2012).
Additive genetic variance (σA2 )
and the proportion of the phenotypic variance explained byσA2 (i.e. narrow sense
heritability; h2 ), reflect the heritable
genetic component of a trait and determine the potential rate of any
evolutionary response to selection acting on the trait (Lande, 1979). A
specific linear mixed effects model called the “animal model” uses
information on the relatedness of individuals with phenotypic data and
is widely used to estimate additive genetic variances of phenotypic
traits of domestic animals as well as wild populations of many species
(Kruuk, 2004; Lynch & Walsh, 1998; Wilson et al., 2010). However, most
animal models assume that the populations under study are genetically
homogeneous, which is often not the case in natural populations, and
this assumption may therefore introduce biases in estimates (Muff,
Niskanen, Saatoglu, Jensen, & Keller, 2019; Wolak & Reid, 2017). A
recent extension called genetic groups animal model (GGAM) enables us to
account for genetic admixture within and between populations and allows
estimating heterogeneous and population-specific mean genetic values
(basic GGAM; Wolak & Reid, 2017) and additive genetic variances
(extended GGAM; Aase, Jensen, & Muff, 2022; Muff et al., 2019).
Genome wide association studies (GWAS) are commonly performed to
investigate underlying genetics of phenotypic traits and to detect
Quantitative Trait Loci (QTL; Korte & Farlow, 2013). In relation to
dispersal, it has for instance been shown that a foraging gene inDrosophila melanogaster is linked with locomotion behavior,
causing adults with the dominant ‘rover’ allele to have longer dispersal
distances (Edelsparre, Vesterberg, Lim, Anwari, & Fitzpatrick, 2014).
Similarly, the Pgi gene in the Glanville fritillary butterfly
(Melitaea cinxia ) codes for a metabolic enzyme associated with
cellular energetics (Mattila & Hanski, 2014), and has an allelic
variant that causes a higher flight metabolic rate and dispersal
propensity (Haag, Saastamoinen, Marden, & Hanski, 2005; Niitepõald et
al., 2009; Niitepõld, Mattila, Harrison, & Hanski, 2011). However,
research on genetic variation in dispersal in natural populations, as
well as other complex life history traits, indicates that underlying
genetic variation is often caused by many genes of small effect (i.e.
are polygenic; Saastamoinen et al., 2018; Tiffin & Ross-Ibarra, 2014;
Zera & Brisson, 2012). Polygenic traits may covary with several
different fitness traits and are often influenced by multiple
environmental factors and can hence show complex evolutionary
trajectories (Remington, 2015).
Studies on the genetic architecture of dispersal pave the road to a
better understanding of the ecological and evolutionary consequences of
dispersal and movement in fragmented populations and species invasions,
and hence the capacity to spread and ultimately survive in the face of
environmental change (Saastamoinen et al., 2018). In the present study,
we used successful natal dispersal between islands as the phenotypic
trait in order to investigate the heritable genetic basis of dispersal
in an insular metapopulation of a small passerine bird, the house
sparrow (Passer domesticus ). Previous studies have shown spatial
differences in dispersal rates related to island habitat type (Ranke et
al., 2021; Saatoglu et al., 2021). Initially we therefore assumed that
the heritable genetic variation in dispersal was similar across islands
but allowed the mean genetic values of dispersal to differ between
island habitat types, and used a basic genetic groups animal model
(basic GGAM) to estimate the σA2 of dispersal probability. Subsequently, we used an extended GGAM to
allow for different σA2 of
dispersal for the two habitat types. Lastly, we used GWAS to identify
genes that might explain variation between individuals in dispersal
probability. To achieve these goals, we used high-quality information on
dispersal and high-density genome-wide single nucleotide polymorphism
(SNP) genotype data from over 2500 individuals in a long-term study of
house sparrows on islands in a metapopulation off the coast of northern
Norway, where relatedness is available through a genetically determined
multi-generational pedigree (Lundregan et al., 2018; Niskanen et al.,
2020; Saatoglu et al., 2021).