Using genomic tools to understand and manage adaptation to climate - - PowerPoint PPT Presentation

Using genomic tools to understand and manage adaptation to climate

Sally Aitken Department of Forest and Conservation Sciences University of British Columbia

Climate change is already affecting forests globally

Heat and drought Insects (mountain pine beetle) Diseases (Swiss needle cast) Extreme events

Historical range and climate Climatic niche distribution with warming Lagging edge population extirpation Adaptation over generations using standing variation and gene flow Natural migration from leading edge

Natural population responses

Climatic gradient warm cold

Assisted gene flow Assisted species migration Ex situ conservation Monitoring

Conservation and Management Options

Historical range and climate Climatic niche distribution with warming Climatic gradient warm cold

Types of assisted migration

Long-distance introduction of exotics

Assisted gene flow: Intentional translocation of individuals within a species range to facilitate adaptation to anticipated local conditions (Aitken&Whitlock 2013)

Seed zones based on local populations no longer match genotypes with climates

BC Alberta BC Alberta

Assisted gene flow (AGF) prescriptions needed for natural seedlots, selectively bred material and novel plants.

Current 2050s Mean annual temperature (MAT)

Historical range and climate Climatic niche distribution with warming Climatic gradient warm cold

Risks of actions

• Altered ecosystems
• Maladaptation if predictions

are wrong

• Lack of public acceptance
• Low productivity
• Dropping timber

supply

• Increased pests

Risks of inaction

Focal biological scale varies with discipline

Genotypes

• Clonal forestry
• Plantation

forestry

• Short rotation

crops

• Breeding
• Genetic

modification

• Homogeneous

environments

Populations

• Reforestation and

restoration

• Breeding
• Local adaptation
• Adaptive capacity
• Genetic diversity
• Variable

environments

Species

• Species selection
• Natural or

assisted range shifts

• Climatic niche

projections

• Endangered

species conservation

Ecosystems

• Insects
• Diseases
• Mutualists
• Species diversity
• Habitat
• Forest

management

My focus: Populations of long lived, widespread temperate and boreal species

Outline

• Genetics of climate adaptation
• Genomic approaches for guiding

reforestation in a changing climate

• Potential of biotech approaches for climate

adaptation, insect and pest resistance

• Diversity as a tool to mitigate uncertainty of

effects of climate change

California 12oC Oregon 11oC

• ---British Columbia----
• -------Alaska-------------

Moderate to strong local adaptation to climate in temperate & boreal trees; less evidence of

• ther environmental drivers

10oC 8oC 7oC 5oC 4oC 4oC 3oC Sitka spruce planted in Vancouver provenance trial

Aitken and Bemmels. 2016. Evol. Appl.

Bud$$$$$$$set$ Winter'

Growth$ Dormancy$

Environmental$effect$of$ climate$change$ $ GeneAc$effect$of$AGF$from$ warmer$provenance$ Summer' Bud$flush$ Bud$flush$

$ $

Aitken and Bemmels 2016 Evol. Appl.

Seasonal growth and dormancy cycle influenced by both genetics and environment

Frost$injury$ Drought$ Insects$ Diseases$ $ Stresses&

Stresses Frost injury Drought Insects Diseases

Lodgepole pine ‘Interior’ spruce complex

Picea glauca hybrid

• P. engelmannii

Pinus contorta ssp. latifolia

AdapTree Project: Analyzed phenotypes and genomes for >250 populations of two species

LANDSCAPE

G E N O M I C S

E

C O L O G I C A L G E N O M I C S

Genomic data

POPULATION

GENOMICS

ECOLOGICAL

GENETICS

QUANTITATIVE

GENETICS

SPATIAL

ANALYSIS

Genotype-environment (GEA) Genotype-phenotype (GWAS)

Genomics can rapidly assess climate adaptation

Phenotype-environment (PEA)

Environmental data Phenotypic data

GEA and GWAS identify climate- associated genetic markers (SNPs)

Seedlings phenotyped for growth, phenology, cold hardiness, heat and drought stress response in growth chambers or outdoor common gardens

HOT WET HOT DRY HOT HEAT STRESS HOT DRY

HEAT STRESS

MILD COLD

PiaSmets

Interior spruce

Fall cold injury – lodgepole pine

Strong associations between cold hardiness and phenology phenotypes and low temperatures

281 populations sampled Liepe et al. 2016. Evolutionary Applications

30-year extreme minimum temp.

Fall cold injury – lodgepole pine

Pine and spruce show very similar phenotypic patterns of adaptation to low temperatures

Liepe et al. 2016. Evolutionary Applications

Fall cold injury – interior spruce

1) Exon-oriented sequence capture (Nimblegen 40-50Mb capture)

• ~23,000 genes per species
• >1 million single nucleotide polymorphisms (SNPs) per species
• ~700 trees/species

2) ~50K SNP Affymetrix array with adaptation candidates

• ~19,700 genes
• ~32,000 high-quality SNPs
• 2,500-4,000 trees/species

Exome capture and SNP arrays used for genotyping due to large genome size

(Suren et al. 2016. Mol Ecol Res)

Lodgepole pine Interior spruce

Environment- environment associations Genotype- environment associations Genotype- genotype associations

Genotype- environment associations show complex patterns of climate adaptation

Lotterhos et al. Biorxiv 3 contigs 28 contigs 21 contigs 28 contigs

801 SNPs in 117 “top candidate” genes of lodgepole pine

A) “Multi” cluster (SNP from contig #1) A) “Aridity” cluster (SNP from contig #8)

Mean Annual Temperature Annual Heat:Moisture Index

We risk oversimplifying climate adaptation by focusing on a few genes or climatic factors

e.g., patterns of variation for individual SNPs (lodgepole pine)

• K. Lotterhos et al. Biorxiv

260 pine “top candidate” genes 450 spruce “top candidate” genes

47 “top candidate” genes for adaptation to low temperatures shared by pine and spruce

Genetic complexity of adaptation to low temperatures in pine and spruce:

A comparative approach

Yeaman et al. 2016 Science

Provenance Trial at 54°N

Genomic analyses identify the same climatic drivers of local adaptation as provenance trials

AHM bFFP CMD DD_0 DD5 eFFP EMT Eref EXT FFP MAP MAT MCMT MSP MWMT NFFD PAS SHM TD

0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.5

2

R2 for GEA prediction Pine

0.2 0.4 0.6

R2 from provenance trial data

(Unpublished data)

MacLachlan et al. 2017. Tree Genetics and Genomes; MacLachlan 2017 PhD dissertation

Genomic data recapitulates climatic clines for phenotypic traits

Lodgepole pine breeding zones Measured cold injury

−1 1 2 3 4 5 0.40 MAT 0.55

Cold injury (%)

Frequency of Positive Effect Alleles −1 1 2 3 4 5 0.40 0.45 0.50 0.55

Cold Injury SNPs

Natural r2 = 0.76 Selected r2 = 0.87

MAT 0.55

• Freq. of cold injury

associated alleles

• Freq. of cold injury alleles

Lodgepole pine breeding zones

100 120 140 160 180 200 220 240 0.000 0.005 0.010 0.015 0.020

Natural Selected Height

Wilcoxon rank sum p < 0.0001

120 140 160 180 200 220 240 260 0.000 0.005 0.010 0.015 0.020

Natural Selected Cold Injury Number of Positive Effect Alleles

Selection and breeding for faster growth results in small changes in allele frequency at hundreds of genes

MacLachlan et al. 2017. Tree Genetics and Genomes; MacLachlan 2017 PhD dissertation

CoAdapTree project targets adaptation to climate and pathogens in four conifers

(S. Aitken, S. Yeaman, R. Hamelin, co-project leaders)

Douglas-fir and western larch Lodgepole pine and jack pine

Climate adaptation & Swiss needle cast tolerance Climate adaptation Dothistroma needle blight resistance & tolerance Climate adaptation

Pathogen resistance/tolerance:

CoAdapTree studying genetics of fungi causing Swiss needle cast and Dothistroma needle blight as well as hosts

Identify candidate genes and population variation for disease resistance or tolerance. Define pathogenicity zones and predict disease response to climate change

Risk 1996- 2006 Risk 2050s

One source of uncertainty: Climate novelty

Mahony et al. 2017. Global Change Biology

Longer rotations…greater uncertainty

• Species with long rotations will

experience more climate change and more uncertainty

• Species diversity and genetic diversity

provide insurance

• Little opportunity for biotech solutions

for resistance to insects or diseases attacking later in life as testing would take decades

• Opportunities for biotech applications

will be greatest for short-rotation fiber farms

Understanding values and perceptions of stakeholders key to implementing new technologies

Acceptance of forest management interventions (N=1,544 households)

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

• Sc. 1

Sc.2

• Sc. 3
• Sc. 4
• Sc. 5
• Sc. 6

Proportion on respondents

Completely reject Tentativeley reject Tentatively Accept Completely Accept Total Accept

• Sc. 1
• Sc. 2
• Sc. 3
• Sc. 4
• Sc. 5
• Sc. 6

Do nothing, natural regen. Local seeds, no breeding Local seeds, with breeding Assisted gene flow Assisted species migration GMOs

Hajjar et al. 2014. Can. J. For. Res.

Summary

• Native tree populations are locally adapted to climate
• Assisted gene flow (AGF) is needed to maintain health and productivity,

but can’t move too far too fast

• Genomic approaches generate detailed knowledge of climate adaptation

quickly

• Adaptation to climate is highly polygenic and multidimensional; little
• pportunity for GM
• GM approaches may help address expanding diseases and insects with

warming in conjunction with breeding and AGF in some situations

• Deploying diverse genetic backgrounds is key for addressing uncertainty

with climate change

AdapTreeTeam: Andreas Hamann (Co-PL) – Geospatial anal. (U of A) Jason Holliday -- Re-sequencing (Virginia Tech) Loren Rieseberg -- Bioinformatics (UBC) Michael Whitlock -- Population structure (UBC) Rob Kozak –Values and perceptions (UBC) Tongli Wang -- Climatology (UBC) Sam Yeaman – Bioinformatics (UBC) Kay Hodgins – Bioinformatics (Monash U) Katie Lotterhos – Pop. structure (Wake Forest U) Simon Nadeau – Population genetics (UBC) Haktan Suren – Association gen. (Virginia Tech) Jon Degner – Hybrid analysis (UBC) CoAdapTreeTeam (partial list): Sam Yeaman – Genomics (U of Calgary) Richard Hamelin – Pathology (UBC and Laval) Juergen Ehlting, Ingo Ensminger, Shannon Hagerman, Rob Kozak, Loren Rieseberg, Mike Whitlock, Andreas Hamann Ian MacLachlan – Effects of breeding (UBC) Katharina Liepe – Geospatial analysis (U of A) Kristin Nurkowski – Genomics (UBC/Monash) Laura Gray – Phenotypic analysis (U of A) David Roberts – Geospatial analy. (U of A) Robin Mellway – Phenotyping and functional genetics (UBC) Jeremy Yoder – Population genomics (UBC) Gina Conte – Bioinformatics (UBC) Pia Smets – Project management (UBC) Reem Hajjar – Social science (UBC) Collaborators (partial list): FLNRO: Greg O’Neill, Nick Ukrainetz, Barry Jaquish, Trevor Doerksen, Michael Stoehr FGC: Jack Woods, Brian Barber USFS: Brad St. Clair, Richard Cronn UC Davis: David Neale (PineRefSeq) UBC: Joerg Bohlmann (white spruce genome)

Summary

• Native tree populations are locally adapted to climate
• Assisted gene flow (AGF) is needed to maintain health and productivity,

but can’t move too far too fast

• Genomic approaches generate detailed knowledge of climate adaptation

quickly

• Adaptation to climate is highly polygenic and multidimensional: Little
• pportunity for GM
• GM approaches may help to address expanding impact of diseases and

insects with warming in conjunction with breeding and AGF

• Deploying diverse genetic backgrounds is key for addressing uncertainty

with climate change