The response of populations and species to changing conditions determines how community composition will change functionally, including via trait shifts. Selection from standing variation has been suggested to be more efficient than acquiring new mutations. Yet, studies on community trait composition and trait selection largely focus on phenotypic variation in ecological traits, whereas the underlying genomic traits remain understudied. Using a genome‐explicit, niche‐ and individual‐based model, we address the potential interactions between genomic and ecological traits shaping communities under an environmental selective forcing, namely temporal positively autocorrelated environmental fluctuation. In this model, all ecological traits are explicitly coded by the genome. For our experiments, we initialized 90 replicate communities, each with ca 350 initial species, characterized by random genomic and ecological trait combinations, on a 2D spatially explicit landscape with two orthogonal gradients (temperature and resource use). We exposed each community to two contrasting scenarios: without (i.e. static environments) and with temporal variation. We then analyzed emerging compositions of both genomic and ecological traits at the community, population and genomic levels. Communities in variable environments were species poorer than in static environments, and populations more abundant, whereas genomes had lower genetic linkage, mean genetic variation and a non‐significant tendency towards higher numbers of genes. The surviving genomes (i.e. those selected by variable environments) coded for enhanced environmental tolerance and smaller biomass, which resulted in faster life cycles and thus also in increased potential for evolutionary rescue. Under temporal environmental variation, larger, less linked genomes retained more variation in mean dispersal ability at the population level than at genomic level, whereas the opposite trend emerged for biomass. Our results provide clues to how sexually‐reproducing diploid plant communities might react to variable environments and highlights the importance of genomic traits and their interaction with ecological traits for eco‐evolutionary responses to changing climates.

After a slow start in the 1980s, individual-based models (IBMs, also known as agent-based models or mechanistic simulation models) have found growing acceptance as a tool of ecological research (DeAngelis & Grimm, 2014;Grimm & Railsback, 2005).Over the past decade, they have been continuously expanded to simulate increasingly complex macroecological (e.g. Cabral &

Species invasions are highly complex phenomena, influenced by several interacting factors, such as species traits, disturbance, or evolutionary history (Enders et al., 2020;Theoharides & Dukes, 2007). Gaining an understanding of these factors is necessary to understand the whole invasion process (Fleming & Dibble, 2015) and establish effective countermeasures (Novoa et al., 2020). Yet, the relative importance of various factors is difficult to derive from studies focusing only on single invasion events (Catford et al., 2009). Considering the impending global change scenarios and increased rate of biotic exchange, however, generalizable findings about biological invasions are still urgently needed (van Kleunen et al., 2015).

Introgressive hybridization is a process that enables gene flow across species barriers through the backcrossing of hybrids into a parent population. This may make genetic material, potentially including relevant environmental adaptations, rapidly available in a gene pool. Consequently, it has been postulated to be an important mechanism for enabling evolutionary rescue, that is the recovery of threatened populations through rapid evolutionary adaptation to novel environments. However, predicting the likeli-