A Trade-Off between Robustness to Environmental Fluctuations and Speed of Evolution.

AbstractUnderstanding how a species' life history affects its capacity to cope with environmental changes is important in the context of rapid climate changes. Reinterpreting previous results from a well-developed theoretical framework, we show that a trade-off exists between a species' ability to genetically adapt to long-term gradual environmental changes and its ability to demographically resist short-term environmental perturbations, causing variation in its vital rates. Surprisingly, this important insight has not been made formally explicit before. Choosing archetypal life histories along the fast-slow pace-of-life continuum and modeling their eco-evolutionary dynamics, we further show that long-lived species have larger demographic robustness to interannual fluctuations but limited trait evolutionary responses in gradually changing environments. In contrast, short-lived species had larger evolvability but reduced demographic robustness. This trade-off bears heavily on extinction probabilities of populations tracking fast trait changes in stochastic environments. Faster trait evolution in short-lived species came at the expense of their higher sensitivity to short-term fluctuations, causing higher extinction rates than for long-lived species. Long-lived species persisted better on short timescales but built maladaptation and an extinction debt over time. This work shows how modeling species' eco-evolutionary dynamics can help to assess species vulnerability to environmental changes.

epiGBS2: Improvements and evaluation of highly multiplexed, epiGBS-based reduced representation bisulfite sequencing.

Several reduced-representation bisulfite sequencing methods have been developed in recent years to determine cytosine methylation de novo in nonmodel species. Here, we present epiGBS2, a laboratory protocol based on epiGBS with a revised and user-friendly bioinformatics pipeline for a wide range of species with or without a reference genome. epiGBS2 is cost- and time-efficient and the computational workflow is designed in a user-friendly and reproducible manner. The library protocol allows a flexible choice of restriction enzymes and a double digest. The bioinformatics pipeline was integrated in the Snakemake workflow management system, which makes the pipeline easy to execute and modular, and parameter settings for important computational steps flexible. We implemented bismark for alignment and methylation analysis and we preprocessed alignment files by double masking to enable single nucleotide polymorphism calling with Freebayes (epiFreebayes). The performance of several critical steps in epiGBS2 was evaluated against baseline data sets from Arabidopsis thaliana and great tit (Parus major), which confirmed its overall good performance. We provide a detailed description of the laboratory protocol and an extensive manual of the bioinformatics pipeline, which is publicly accessible on github (https://github.com/nioo-knaw/epiGBS2) and zenodo (https://doi.org/10.5281/zenodo.4764652).

msGBS: A new high-throughput approach to quantify the relative species abundance in root samples of multispecies plant communities.

Plant interactions are as important belowground as aboveground. Belowground plant interactions are however inherently difficult to quantify, as roots of different species are difficult to disentangle. Although for a couple of decades molecular techniques have been successfully applied to quantify root abundance, root identification and quantification in multispecies plant communities remains particularly challenging. Here we present a novel methodology, multispecies genotyping by sequencing (msGBS), as a next step to tackle this challenge. First, a multispecies meta-reference database containing thousands of gDNA clusters per species is created from GBS derived High Throughput Sequencing (HTS) reads. Second, GBS derived HTS reads from multispecies root samples are mapped to this meta-reference which, after a filter procedure to increase the taxonomic resolution, allows the parallel quantification of multiple species. The msGBS signal of 111 mock-mixture root samples, with up to 8 plant species per sample, was used to calculate the within-species abundance. Optional subsequent calibration yielded the across-species abundance. The within- and across-species abundances highly correlated (R2 range 0.72-0.94 and 0.85-0.98, respectively) to the biomass-based species abundance. Compared to a qPCR based method which was previously used to analyse the same set of samples, msGBS provided similar results. Additional data on 11 congener species groups within 105 natural field root samples showed high taxonomic resolution of the method. msGBS is highly scalable in terms of sensitivity and species numbers within samples, which is a major advantage compared to the qPCR method and advances our tools to reveal hidden belowground interactions.