Pitfalls and windfalls of detecting demographic declines using population genetics in long-lived species

Detecting recent demographic changes is a crucial component of species conservation and management, as many natural populations face declines due to anthropogenic habitat alteration and climate change. Genetic methods allow researchers to detect changes in effective population size (Ne) from sampling at a single timepoint. However, in species with long lifespans, there is a lag between the start of a decline in a population and the resulting decrease in genetic diversity. This lag slows the rate at which diversity is lost, and therefore makes it difficult to detect recent declines using genetic data. However, the genomes of old individuals can provide a window into the past, and can be compared to those of younger individuals, a contrast that may help reveal recent demographic declines. To test whether comparing the genomes of young and old individuals can help infer recent demographic bottlenecks, we use forward-time, individual-based simulations with varying mean individual lifespans and extents of generational overlap. We find that age information can be used to aid in the detection of demographic declines when the decline has been severe. When average lifespan is long, comparing young and old individuals from a single timepoint has greater power to detect a recent (within the last 50 years) bottleneck event than comparing individuals sampled at different points in time. Our results demonstrate how longevity and generational overlap can be both a hindrance and a boon to detecting recent demographic declines from population genomic data.

Methods
All data for this publication were generated via evolutionary simulations in SLiM. Here, we archive all scripts necesarily to generate, analyze, and visualize the results presented in Clark et al. 2024. 

First, we performed simulations in SLiM using a perennial and annual model for a variety of average lifespans (for the perennial model), and bottleneck severities. The output of these simulations is  (1) a .tree file contain the geneological history of the population, from which we will extract information about genetic diversity, (2) individual-based metadata for all individuls alive during the simulation sampling time: the generation number, individual pedigree id and the individual's age, (3) Census population size information about the population at each generation in the sampling period. 

Second, we used tskit, msprime, and pyslim to load and process .tree files as tree sequences. We then loop through focal sampling points in the tree sequence, and sampling individuals to perform age and temporal comparisons. Genetic diversity data from the sampled bins is exported as .txt files. 

Finally, genetic diversity data is loaded in R, permutation tests are performed to test for significant differences in genetic diversity between bins, and figures are created.