Impact of climate

oscillations on the

population genomics of

alpine cold-adapted endemic

plants and their pollinators

Agriculture, Environment and Bioenergy PhD Course. XXXV cycle - 2019/2020February 17th, 2020

SARA VILLA - R12508TUTOR: PROF. SIMON PIERCE

CO-TUTOR: PROF. MATTEO MONTAGNA

EXTERNAL TUTOR: PROF. CRISTIANO VERNESI

My background

Bachelor’s and Master of Science

degrees in Natural Sciences

Science teacher at a high school

(2017-2018, 2018-2019)

- PSR 2014-2020:

application of

naturalistic

engineering

techniques for

the restoration

of traditional

springs

Collaboration in projects:

- «BIOTER: analisi della diversità BIOlogica e funzionale dei TERrazzamenti nel

Parco Nazionale della Val Grande»: vegetation surveys on terraced landscapes

Case study and Problems Investigate the response of Campanula raineri Perp. to climate oscillations during

the Quaternary and predict the consequences of current increasing temperature.

Case study and Problems Calcareous-dolomitic cliffs, 600-2000 m a.s.l.

Background

Fragmented distributions of cold-adapted species after temperature oscillations

during the Quaternary (Walther et al., 2002, 2005; Ikeda et al., 2008; Lenoir et

al., 2008).

Isolation is a favourable condition for genetic differentiation (Higashi et al.,

2012; Wang et al., 2012).

Application of population genomics in Phylogeography (Emerson et al., 2010).

Use of Species Distribution Modeling (SDM) to infer past distribution and to

predict future range development for a target species, according to its

ecological requirements and climate projections (Engler et al., 2011; Gogol-

Prokurat, 2011; You et al., 2018; Gargiulo et al., 2019).

Evolution of showy flowers in alpine angiosperms in order to attract pollinators

and ensure reproductive success (Peng et al., 2012).

Hypotheses

Climate oscillations are associated with changes in the distributional range of

Campanula raineri (and potentially additional species, i.e. Primula glaucescens);

According to the worst-case scenarios predicted by climate change models, the

current distribution area of the target species will no longer be ecologically

suitable by 2100;

Additional potentially suitable areas for the species will emerge;

Species Distribution Models (SDM) are consistent with the genetic structure of

populations and with migration models;

C. raineri forms part of a pollination network and benefits from the presence of

plants with similar flowers that support pollinator populations.

Materials and methodsReconstruction of species history:

DNA extraction from leaves (using CTAB)

Genome-wide genotyping using 2b-RAD (Wang et al., 2012)

Genomic data analysis through bioinformatics tools:

Software Functions References

STACKS2 Identification of SNPs Rochette et al., 2019

BayeScan, LOSITANIdentification and removal of SNPs under

positive selectionAntao et al., 2008; Foll & Gaggiotti, 2008

popART, STRUCTURE and R packages -APE, Poppr, pegas-

Definition of population structure and demographic analysis

Paradis, 2010; Pritchard et al., 2000; Kamvar et al., 2014; Paradis et al., 2004; Leigh & Bryant, 2015

BEAST 2Reconstruction of demographic history

and elaboration of the most likely framework

Bouckaert et al., 2019

MIGRATEIdentification of the most likely migration

patternBeerli & Palczewski, 2010

Materials and methods

Species Distribution Modeling (SDM):

Definition of environmental requirements of C. raineri.

Assessment of habitat suitability for C. raineri (Maxent, Gogol-Prokurat, 2011;

biomod2, Gargiulo et al.,2019; Hijmans & Graham, 2006)

Elaboration of species distribution models based on past to future demographic

trends and habitat suitability

Gargiulo et al., 2019. Journal of Biogeography, 46: 526–538. Reproduced with permission of John Wiley and Sons.

Gargiulo et al., 2019. Journal of Biogeography, 46: 526–538. Reproduced with permission of John Wiley and Sons.

Materials and methodsDefinition of pollination network:

Field observations and eDNA metabarcoding from C. raineri and similar flowers

(Thomsen & Sigsgaard, 2018)

Thomsen & Sigsgaard,

2019. Ecology and

Evolution,

9: 1665–1679.

Permission granted by

the Creative Commons

Attribution License John

Wiley and Sons.

Materials and methodsDefinition of pollination network:

Field observations and eDNA metabarcoding from C. raineri and similar flowers

(Thomsen & Sigsgaard, 2018)

Species determination from pollen on C. raineri stigma

Materials and methodsDefinition of pollination network:

Field observations and eDNA metabarcoding from C. raineri and similar flowers

(Thomsen & Sigsgaard, 2018)

Species determination from pollen on C. raineri stigma

Species determination from pollen on Bombus spp.

individuals

Current activities

Bibliographic research

- C. raineri distribution and ecology

- Optimization of DNA extraction protocol

- SNPs identification and applications, genome-wide genotyping using 2b-RAD

- Bioinformatic tools

- Species Distribution Modeling

Optimization of CTAB protocol for DNA extraction from C. raineri

Current activities

2019

sampling

Optimization of CTAB protocol for DNA extraction from C. raineri

Current activities

Optimization of CTAB protocol for DNA extraction from C. raineri

Current activities

Identification of sampling locations for 2020 spring-summer 2019 sampling

Corni di Canzo

Parlasco

Pizzo Arera e

M. Alben

Concarena

Current activities

Identification of potential pollinator-supporting species

Amadej Trnkoczy

Gentiana clusii

Daniela Longo

Gentianopsis ciliata

Mario Castagna

C. cochleariifolia

Daniela Longo Patrizia Ferrari

Viola dubyana

Aquilegia einseleana

Simon Pierce

Current activities

Applications

Conservation strategies

Assisted migration for C. raineri in suitable areas

Ex-situ propagation, reintroduction and population reinforcement

Transfer of the methodology to additional species … future projects?

ReferencesAntao T et al. 2008. BMC Bioinform 9,323

Beerli P, Palczewski M. 2010. Genetics 185(1), 313-326

Bouckaert R et al. 2019. PLoS Comput Biol 15(4),

e1006650

Emerson KJ et al. 2010. PNAS 107(37), 16196-16200

Engler R et al. 2011. Global Change Biology 17, 2330–2341

Foll M, Gaggiotti O. 2008. Genetics 180, 977-993

Gargiulo R et al. 2019. J Biogeogr 46,526–538

Gogol-Prokurat M. 2011. Ecological Applications 21(1), 33–

47

Higashi H et al. 2012. J Plant Res 125, 223–233

Hijmans RJ Graham CH. 2006. Global Change Biol 12,

2272–2281

Ikeda H et al. 2008. J Biogeogr 35, 791–800

Kamvar ZN et al. 2014. PeerJ, DOI 10.7717/peerj.281

Leigh JW, Bryant D. 2015. Methods Ecol Evol 6, 1110–1116

Lenoir J et al. 2008. Science 320, 1768-1771

Paradis E 2010. Bioinformatics 26(3), 419–420

Paradis E et al. 2004. Bioinformatics 20(2), 289–290

Peng D et al. 2012. Alpine Bot 122, 65–73

Pritchard JK et al. 2000. Genetics 155(2), 945-959

Rochette NC et al. 2019. Mol Ecol 28, 4737– 4754

Thomsen PF, Sigsgaard EE. 2018. Ecol Evol 9, 1665–1679

Walther G et al. 2002. Nature 416, 389–395

Walther G et al. 2005. Science 16, 541-548

Wang S et al. 2012. Nat Methods 9(8), 808-810

You J et al. 2018. Sci Rep 8(5879), DOI 10.1038/s41598-

018-24360-9