Interactions between breeding system and ploidy affect niche breadth in Solanum

Abstract

Understanding the factors driving ecological and evolutionary interactions of economically important plant species is important for agricultural sustainability. The geography of crop wild relatives, including wild potatoes ( Solanum section Petota ), have received attention; however, such information has not been analysed in combination with phylogenetic histories, genomic composition and reproductive systems to identify potential species for use in breeding for abiotic stress tolerance. We used a combination of ordinary least-squares (OLS) and phylogenetic generalized least-squares (PGLM) analyses to identify the discrete climate classes that make up the climate niche that wild potato species inhabit in the context of breeding system and ploidy. Self-incompatible diploid or self-compatible polyploid species significantly increase the number of discrete climate classes within a climate niche inhabited. This result was sustained when correcting for phylogenetic non-independence in the linear model. Our results support the idea that specific breeding system and ploidy combinations increase niche breadth through the decoupling of geographical range and niche diversity, and therefore, these species may be of particular interest for crop adaptation to a changing climate.

Wild relatives of potato may bolster its adaptation to new niches under future climate scenarios

“…These self‐compatible, diploid PWRs exist in the current climate primarily identified by lower PC1 (temperature) and PC2 (precipitation) values (Table S1). It was unexpected that self‐compatible diploid species were in the novel climate space as their climate niche is expected to be smaller than other ploidy‐mating system combinations (Fumia et al, 2022), this indicates that the specialized nature of the self‐compatible diploid populations is becoming more important and more represented in future climate predictions.…”

“…The Köppen–Geiger divides climate regimes into a discrete three‐tiered system (five main, 30 sub and sub–sub tiers) based on main climate, seasonal precipitation, and temperature (reviewed in Rubel & Kottek, 2010). Further, the regression coefficients for climate niche diversity as well as phylogenetic relatedness were sourced for PWR from Fumia et al (2022).…”

“…The eight GCMs were BCC-CSM2-MR, CNRM-CM6-1, CNRM-ESM2-1, CanESM5, IPSL-CM6A-LR, MIROC-ES2L, MIROC6, and MRI-ESM2-0. Current and future climatic information were extracted for each potato and PWR occurrence point using the extract function in the raster package of R. Data for the Köppen-Geiger climate classes of PWR and life history traits were derived from Fumia et al (2022). The Köppen-Geiger divides climate regimes into a discrete three-tiered system (five main, 30 sub and sub-sub tiers) based on main climate, seasonal precipitation, and temperature (reviewed in Rubel & Kottek, 2010).…”

“…Environmental plasticity (ep) represents the correlation between the percentage overlap of a PWR species over the current climate of potato growing regions with the percentage overlap of a PWR species over the future forecasted climate of potato growing regions (adjusted for each SSP and year combination) (Figure 1). Due to the interspecific nature of potential crosses, we adjust the correlation between time points with crossability using two aspects: (1) to adjust for the evolutionary role in climatic diversity, we use the regression coefficient for niche divergence among PWR species with different life history trait interactions (see Fumia et al, 2022); and (2) to adjust for the evolutionary role in crossability, we use the chloroplast genome genetic distance between all PWR and S. tuberosum (Fumia et al, 2022), representing the evolutionary history (eh) (Figure 1). The evolutionary history (eh) represents the regression output of life-history trait influence upon climatic variation as specifically the intercept +or -the slope value by a PWRs life-history trait combination (Fumia et al, 2022).…”

“…Due to the interspecific nature of potential crosses, we adjust the correlation between time points with crossability using two aspects: (1) to adjust for the evolutionary role in climatic diversity, we use the regression coefficient for niche divergence among PWR species with different life history trait interactions (see Fumia et al, 2022); and (2) to adjust for the evolutionary role in crossability, we use the chloroplast genome genetic distance between all PWR and S. tuberosum (Fumia et al, 2022), representing the evolutionary history (eh) (Figure 1). The evolutionary history (eh) represents the regression output of life-history trait influence upon climatic variation as specifically the intercept +or -the slope value by a PWRs life-history trait combination (Fumia et al, 2022). The ordinary least squares regression referred to uses the count of climate classes as the response with individual life-history trait combinations as the predictors (e.g., asexual diploid, self-compatible diploid, self-incompatible diploid, and selfcompatible polyploid), of which the associated slope value is used in eh dependent upon this biology of the PWR species.…”