Project Outline

Genomes may vary in DNA content (e.g. amplification of repeated sequences) or in chromosome number (e.g. dysploidy or polyploidy), which can bear adaptive value. The increase in DNA content and chromosome number may decrease gene linkage, affect gene regulation or repurpose the extra gene copies. Genomic processes may occur at higher rates under stressful conditions. For example, plant individuals at stressful conditions have a higher production rate of unreduced gametes, promoting autopolyploidization. However, it is little understood how different genomic processes interact and how adaptive are the resulting genomic variants to population and species survival. Shedding light to these gaps will unravel how species may respond to impending environmental change, which will trigger genomic variants.

Objectives

To assess how variation in DNA content and chromosome number affect eco-evolutionary processes under stressful, changing environmental conditions. Specifically, the project will address 1) whether an increase in DNA content increases individual fitness, population persistence and species survival, caused at random or at specific areas of the genome; 2) whether autopolyploidy has similar effects; 3) whether variation of DNA content and chromosome number have synergetic effects when triggered together; 4) identify species more vs. less likely 4.1) to undergo these adaptive processes and 4.2) to respond to environmental change via genomic variants.

Methods

This project takes a mechanistic eco-evolutionary approach by applying a spatially- and genomically-explicit individual-based model (GeMM - Leidinger et al. 2021) to a study system – the Maxillariinae orchids (Moraes et al. 2022). Maxillariinae orchids are a horticulturally relevant group (e.g. Bifrenaria, Lycaste, Maxillaria), with high DNA content and chromosomal variation. The GeMM simulates in a spatially explicit environmental arena diploid plant individuals competing for space and performing life-history processes (growth, reproduction, dispersal, survival). These processes are controlled by genomically coded parameters. The genome contains coding and non-coding base pair sequences and undergoes mutation and recombination during gametogenesis. The number of genes per ecological traits and level of gene linkage is hitherto species-specific. Individual fitness (the match between environmental preferences and local conditions), population persistence and species survival emerge from simulation experiments. The project will extend GeMM to integrate intraspecific variation in DNA content and chromosome number (first two years). The objectives 1-3 will be tackled via experiments varying i) DNA increase across the genome (2^nd year); ii) changes in chromosome number (3^rd year); iii) both processes together (4^th year). The environmental preferences will be given by distribution models of species for which we have DNA content, chromosome number and phylogenetic relationship (Moraes et al. 2022). Ecological traits will be assembled in collaboration with Prof. Moraes. Simulation experiments will explore current or estimated ancestral traits. Emergent distribution ranges as well as genomic properties will be statistically parameterized to real-world data to estimate the rate of each genomic process (e.g. DEoptim R package). Parameterized species will have their genetic and ecological trait combinations compared and will be simulated under stressful conditions, such as pollution, temperature increase and precipitation decrease, to address objective 4.

References

Leidinger L, Vedder D & Cabral JS 2021 Temporal environmental variation may impose differential selection on both genomic and ecological traits. Oikos 130: 1100-1115.

Moraes AP, Engel TBJ, Forni-Martins ER, de Barros F, Felix LP & Cabral JS 2022 Are chromosome number and genome size associated with habit and environmental niche variables? Insights from the Neotropical orchids. Ann Bot 130: 11-25.

Techniques

• Computational programming (e.g. Julia, R)
• Computational project management (e.g. RMarkdown, github)
• Agent-based modelling and mechanistic models (e.g. GeMM)
• Big data analyses and management (e.g. relational data banks)
• Parameter optimization
• Phylogenetic hypotheses
• High performance cluster computing
• Phylogenetic ancestral trait reconstruction and macroevolutionary models
• Phenomenological species distribution models (e.g. Maxent, GAMs, ensemble models)
• Macroecological analyses (e.g. genetic and ecological traits across species)