Genomic assessment of local adaptation in dwarf birch to inform assisted gene flow.

James S. Borrell,Richard A. Nichols,Jasmin Zohren,Richard J. A. Buggs

doi:10.1111/eva.12883

Abstract

When populations of a rare species are small, isolated and declining under climate change, some populations may become locally maladapted. Detecting this maladaptation may allow effective rapid conservation interventions, even if based on incomplete knowledge. Population maladaptation may be estimated by finding genome–environment associations (GEA) between allele frequencies and environmental variables across a local species range, and identifying populations whose allele frequencies do not fit with these trends. We can then design assisted gene flow strategies for maladapted populations, to adjust their allele frequencies, entailing lower levels of intervention than with undirected conservation action. Here, we investigate this strategy in Scottish populations of the montane plant dwarf birch (Betula nana). In genome‐wide restriction site‐associated single nucleotide polymorphism (SNP) data, we found 267 significant associations between SNP loci and environmental variables. We ranked populations by maladaptation estimated using allele frequency deviation from the general trends at these loci; this gave a different prioritization for conservation action than the Shapely Index, which seeks to preserve rare neutral variation. Populations estimated to be maladapted in their allele frequencies at loci associated with annual mean temperature were found to have reduced catkin production. Using an environmental niche modelling (ENM) approach, we found annual mean temperature (35%), and mean diurnal range (15%), to be important predictors of the dwarf birch distribution. Intriguingly, there was a significant correlation between the number of loci associated with each environmental variable in the GEA and the importance of that variable in the ENM. Together, these results suggest that the same environmental variables determine both adaptive genetic variation and species range in Scottish dwarf birch. We suggest an assisted gene flow strategy that aims to maximize the local adaptation of dwarf birch populations under climate change by matching allele frequencies to current and future environments.

Highlights

Climate change is predicted to become a major driver of global biodiversity loss (Bellard et al, 2012; Urban, 2015)
We suggest an assisted gene flow strategy that aims to maximize the local adaptation of dwarf birch populations under climate change by matching allele frequencies to current and future environments
To determine the environmental variables influencing the present and future distribution of 178 dwarf birch in the UK, we developed an environmental niche modelling (ENM) based on 763 resampled fine-scale (≤1 km) 179 records from the period 1960-present

Summary

Introduction

Climate change is predicted to become a major driver of global biodiversity loss (Bellard et al, 2012; Urban, 2015). In some cases plants lack the dispersal ability to keep pace with accelerated climate shifts (Loarie et al, 2009), there is an absence of potential habitat at higher latitudes (McKenney et al, 2007) and altitudes (Engler et al, 2011), or suitable new habitats may be separated by too large distances (Meier et al, 2012) In these cases, conservation managers aiming to prevent extinction of species or populations face a choice between relying on in situ evolution to track the environmental change, or attempting conservation interventions such as assisted migration or assisted gene flow that seeks to enable, facilitate or accelerate adaptation. This approach is often unfeasible for wild organisms with long generation times in need of urgent conservation, meaning that more rapid approaches using genomics are desirable (Williams et al, 2008)

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Conclusion