1 INTRODUCTION
Biodiversity and the function of ecosystems are threatened by global
change drivers such as changes in landuse and climate, as well as
biological invasions (Linders et al., 2019; Sala et al., 2000). Invasive
species alter a wide range of ecosystem services, including
provisioning, regulation, and cultural and supporting functions, and
they are particularly hazardous for biodiversity maintenance, human
welfare, and the economy (Charles & Dukes, 2007; Chytrý et al., 2009;
Hejda, Pyšek, & Jarošík, 2009; Pejchar & Mooney, 2009; Vilà & Ibáñez,
2011). Globalization (e.g., international trade and travel) and climate
change (e.g., global warming, droughts, and floods) can interact, which
can in turn increase the level of biological invasions (Catford,
Jansson, & Nilsson, 2009; Le Maitre, Richardson, & Chapman, 2004;
Pino, Font, Carbó, Jové, & Pallarès, 2005; Seebens et al., 2015). As
the total number of invasive species increases, some sites may host
several alien species (Kuebbing & Nuñez, 2015).
The invasion process is a complex phenomenon, driven by numerous
interacting processes, and the effect of this interaction is highly
contingent on the context (Chamberlain, Bronstein, & Rudgers, 2014;
Frost et al., 2019). Consequently, drivers of plant invasion can vary
depending on the specific region and habitat (Taylor et al., 2016).
Nevertheless, invasions have a common pattern, which can be summarized
as the joint effect of propagule pressure, abiotic characteristics of
the environment, and biotic characteristics of both the invader and
recipient vegetation (Catford et al., 2009), the so-called PAB
framework. Propagule pressure (P) includes dispersal and geographical
constraints, while abiotic characteristics (A) comprise environmental
and habitat constraints and biotic characteristics (B) describe the
internal dynamics of the vegetation and community interactions (Catford
et al., 2009). All these factors operate at different spatial scales
(Czarniecka-Wiera, Szymura, & Kącki, 2020; Milbau, Stout, Graae, &
Nijs, 2009) and are influenced by human activity (Essl et al., 2011). In
practice, different indices can be applied as proxies of propagule
pressure and abiotic and biotic conditions in modelling plant invasion
process (Bazzichetto et al., 2018; Beaury, Finn, Corbin, Barr, &
Bradley, 2020; Chytrý et al., 2008; Szymura, Szymura, Zając, & Zając,
2018).
Related to the propagule pressure, the biological invasion correlates
with many anthropogenic factors, such as density of the communication
network, percentage of urban areas, gardening, and the fragmentation of
natural habitats. Such factors can serve as a proxy of propagule
pressure (Foxcroft, Pickett, & Cadenasso, 2011; Pollnac, Seipel,
Repath, & Rew, 2012; Štajerová, Šmilauer, Brůna, & Pyšek, 2017;
Szymura et al., 2018; Vilà & Ibáñez, 2011). In addition, economic and
demographic variables reflect the intensity of human activities;
therefore, socioeconomic factors such as gross domestic production and
human population density can be important in predicting the invasion
level (Essl et al., 2011; Hulme, 2017; Pino et al., 2005; Pyšek &
Richardson, 2010) because they correlate with trade intensity and
communication network density (Hulme, 2009). Among the abiotic
interactions with the greatest impact on a large spatial scale
(continental, regional), climate is considered the most critical in
limiting the geographic distribution of species (Hulme, 2017; Thuiller,
Richardson, & Midgley, 2007). In terms of resource availability,
invasive species usually prefer productive habitats where they are able
to achieve competitive dominance (Czarniecka-Wiera et al., 2020;
Peltzer, Kurokawa, & Wardle, 2016; Perkins, Leger, & Nowak, 2011). In
addition, environments with high variability in resource availability,
resulting from periodic external supply (e.g., surface runoff) or
destruction of local vegetation that previously used the resources
(e.g., human disturbances, abandonment of agricultural crops), are more
susceptible to invasions than habitats with stable availability of
resources (Davis, Grime, & Thompson, 2000; Kulmatiski, Beard, & Stark,
2006; Rejmánek, 1989). Given the biotic characteristics of the invader
and receipt communities, the limiting similarity hypothesis proposes
that the invasion by alien species will be successful if the native
species of the recipient community differ from the invader in terms of
functional traits and resource requirements (MacArthur & Levins, 1967),
which decreases competition for resources (Funk, Cleland, Suding, &
Zavaleta, 2008). Thus the functional traits of the invader should not
overlap with traits of native plants occurring in the invaded community,
which will allow it to occupy an empty niche and successfully invade the
community (Funk et al., 2008; Hejda & de Bello, 2013). Because some
sites can be invaded by several species simultaneously, determining the
interaction between invaders is critical for understanding their
distribution (Kuebbing & Nuñez, 2015). For example, the local species
assemblage can be driven by a priority effect, and the effect is
particularly strong when interacting species have similar use of
resources (Vannette & Fukami, 2014). In practice, the abundance and
composition of invasive species are also related to landscape
characteristics (e.g., habitat fragmentation, patch size, shape, and
connections), habitat type, landuse, and the composition of the
surrounding landscape because these factors correlate with propagule
pressure and habitat quality and availability (Basnou, Iguzquiza, &
Pino, 2015; Chytrý et al., 2009; González-Moreno et al., 2013; Štajerová
et al., 2017; Szymura, Szymura, & Świerszcz, 2016).
Because of the complexity of biological invasion, better understanding
of the underlying factors and their management is challenging. As tools
for obtaining reliable and repeatable information for biological
analyses as well as nature conservation and management of the invaders,
invasive species distribution models (iSDMs) are considered useful
(Lozano et al., 2020; Zurell et al., 2020). Modelling species’
environmental requirements and mapping their distributions through space
and time help to identify the main introduction pathways and secondary
spread and the areas and landuse types that are more prone to invasion.
These various threads could be woven into a strategy of prevention and
elimination of invasive plant species on a regional scale (Lozano et
al., 2020). The iSDMs are especially useful in the face of accelerating
global changes and data deficiencies, as well as limited research
funding (Yates et al., 2018). The PAB approach, despite its obvious
advantages for selection of explanatory variables and model results
interpretation, has rarely been used within an invasive species
distribution modelling framework (but see Bazzichetto et al., 2018;
Czarniecka-Wiera et al., 2020; Lozano et al., 2020).
Goldenrod species from North America represent successful invaders in
Europe, Asia, Australia, and New Zealand (Gusev, 2015; Szymura &
Szymura, 2013; Ye, Yan, Wu, & Yu, 2019; Zhang & Wan, 2017). In Central
Europe, two invasive Solidago species occur, S. giganteaAiton (giant goldenrod) and S. canadensis L. (Canadian
goldenrod). Due to their high environmental impact, wide range of
distribution, and locally high abundance, invasive Solidagospecies have to be controlled in Europe (Fenesi et al., 2015; Sheppard,
Shaw, & Sforza, 2006; Skórka, Lenda, & Tryjanowski, 2010). They have
been proposed for addition to the list of hazardous alien species that
threaten ecosystems, habitats, or other species in European Union
countries (CABI, 2018; EPPO, 2020; Tokarska-Guzik et al., 2015).
Unfortunately, the eradication of widely established invasive plant
species, such as Solidago , is not feasible. The management
strategies need to integrate different options that account for the
distribution and abundance of the invader, its environmental niche, and
the areas that are likely to experience high impacts (Nagy et al., 2020;
Shiferaw et al., 2019; Woodford et al., 2016). Management needs to
consider intrinsic factors related to the biology and ecology of the
invader, as well as extrinsic environmental factors, such as dispersal
vectors and invasion pathways (Shiferaw et al., 2019).
Solidago canadensis and S. gigantea differ with regard to
ecological niche in their native range and the time of introduction into
Europe. However, previous studies suggest that these two species do not
differ regarding their habitat preferences in Central Europe, and
observed differences in their spatial distribution patterns emerge from
historical contingency and limitation in long-range dispersal. The twoSolidago species occupy different areas and rarely form
mixed-species stands (Szymura & Szymura, 2016). In this study, we aimed
to find the main drivers of Solidago species’ invasion at a
regional scale, using a species distribution model and applying PAB
framework for selection of adequate explanatory variables and for
ecological interpretation of the models. The distribution models can be
used for mapping of invasion probability at a regional level to
facilitate invasion control at a macroecological scale.