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

Author:

Clark Meaghan I.123ORCID,Fitzpatrick Sarah W.123ORCID,Bradburd Gideon S.34ORCID

Affiliation:

1. Department of Integrative Biology Michigan State University East Lansing Michigan USA

2. Ecology, Evolution, and Behavior Program Michigan State University East Lansing Michigan USA

3. W.K. Kellogg Biological Station Michigan State University Hickory Corners Michigan USA

4. Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor Michigan USA

Abstract

AbstractDetecting 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.

Funder

National Institute of General Medical Sciences

Publisher

Wiley

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