Simone VincenziEU Marie Curie FellowUniversity of California Santa Cruz, USPolytechnic of Milan, Italysimonevincenzi.com (publications)

HMS, July 2015

Eco-evolutionary responses to extreme events

Ecology in the 21st century

Past environments

Evolutionary history

Pheno traits

Genetic variation

Climate change Novelenvironment

Individual fitness

Evolution

Population performance

Population size

Persistence

Time

The world is becoming more extreme

2014 NE US cold wave

Akasped via Wikicommons

Lake Michigan, Chicago

2014 California drought

Satellite images from NASA/NOAA

2013-14 European floods

Stefan Penninger via Wikicommons

Passau, Germany

Extreme events are increasingly relevant

Montpellier – September 2014

Parma – October 2014

Adaptations?

UK Department for International Development

FEMA Photo Library

Daniel Mayer via Wikicommons

FEMA Photo Library

Daniel Mayer via Wikicommons

Vincenzi, S., and A. Piotti. 2014. Evolution of serotiny in maritime pine (Pinus pinaster) in the light of increasing frequency of fires. Plant Ecology 215:689–701.

Example from my study system

Marble trout Salmo marmoratus

Gorska

Gorska

Lipovscek

Lipovscek

Zakojska

Zakojska

Zakojska

Lipovscek

Gorska

Extreme events cause population and genetic

bottlenecks

Time

Population and genetic bottleneck

Time

Time

Time

Time

Time

Underreported

Climate trend + extreme events

• Focus on means (e.g. temperature)• Different types of extreme events, e.g.

depending on timescale– Temperature, rainfall over season (climate extremes)– Floods, fires, hurricanes (point extremes)

• Adaptations are theoretically more likely for climate extremes (generation time) – ecological consequences are similar, but not the evolutionary, e.g. evolutionary rescue

• What happens when different extremes interact?• Risk of extinction vortex• We develop a (simulation) model

Concepts/ingredients• Genetic variance for quantitative

trait (adaptation to climate variable)• Selection strength• Mutation --------------------------• Directional trend• Variability of climate variable• Frequency and intensity of point

extremes

Biology

Environment

Environment

Climate variable (e.g. temperature)

tch = time of climate changetinc = time of increasing climate variance

Θ either the optimum phenotype or a stochastic realization of climate

Point extremes• Context-dependent– Frequency – Intensity (i.e. induced mortality)

• Cause same mortality risk to all individuals

• Mortality too high means system entirely dominated by the point extremes

• 30% mortality after selection (after trial and error)

Biology

Genetic variance

• VP = VA + VD + VI + VE

• h2 = VA/VP

• R = h2S breeder’s equation

A quantitative trait is a measurable phenotype that depends on the cumulative actions of many genes and the environmentAl1 Al2

0 1

0 -1

2 1

2 0

Breeder’s equation and insights on the effects of selection

Model quantitative trait• z = a + e– z = phenotype– a = breeding value (or genetic value)– e = environmental effect (from normal

distribution)• a is determined by n (20 chosen)

additive genes • σ of a is set as to have a defined

heritability for the trait (0.1-0.5)• full recombination when mating

Selection

ω = width of fitness function

Selection

Base mortality and effects of the climate variable

Simulations• Simulations last 150 years after populations at

mutation-selection balance (500 individuals)• Offspring at time t become adults and are able to

reproduce at time t + 1 • At the start of each simulation, for each individual a

value of a and e is randomly drawn from their initial distribution.

• Sequence of operations: mortality of adults, mating and reproduction, mutation, mortality of offspring.

• Population extinct if n <2 • Mating pairs are randomly drawn from the pool of

adults• Each pair produces a number Pois(2) offspring• Point extremes induce mortality after selection

Example

No variance no problem

Lag

Examples

Examples

How to analyze the results• Drivers

(i) climate trend(ii) climate variability(iii) frequency of point extremes(iv) selective pressure, genetic variability and amplitude of mutation

• Targets(a) risk of population extinction(b) time to extinction(c) distribution of a single quantitative trait that determines relative fitness(d) changes in additive genetic variance for the quantitative trait

• Simulations with combinations of parameters values

• Parameter values chosen over a weak to strong effect

• n replicates per combination (total 25 600)• Specific hypotheses that limit the

“researcher degrees of freedom”• Standard statistical techniques (linear

regression, GLM)• Effect size (and partial R2) and not statistical

significance to assess importance

How to analyze the results

Specific objectives/hypotheses• 1 - after accounting for their

independent effects, the interaction between climate trend, variability and probability of occurrence of point extremes contribute to determine the ecological and genetic fate of the population

• 2 - greater mutation amplitude reduce the risk of population extinction by increasing genetic variability

Risk of extinctionRisk of extinction

Slow

Fast

Risk of extinction

Shift of mean phenotype Increasing selection strength

Fast mean change

Slow mean change

Shift of mean phenotype

Specific objectives/hypotheses

• 3) A GLM model including population size, selection strength, probability of point extremes and genetic variance for the quantitative trait under selection is able to predict contemporary risk of extinction.

Predict extinctionPredictors: 1) population size, 2) selection strength, 3) probability of point extremes and 4) genetic variance for the quantitative trait under selection

0.92 0.82

Predict extinction

- Population size and additive genetic variance are correlated, effect of genetic variance confounded

- False positive and false negative rates ~7-8% on training and validation datasets, excellent job!

Conclusions• The interaction among climate trend,

variability and probability of point extremes had minor effects

• Probability of occurrence of point extremes only slightly increased risk of extinction

• Stronger selection and greater climate variability increased extinction risk

• A simple model including four ecological, genetic and demographic measures provided excellent prediction of the immediate risk of population extinction.

ReferencesBürger, R., and M. Lynch. 1995. Evolution and extinction in a

changing environment: a quantitative-genetic analysis. Evolution 49:151–163.

Bürger, R., and M. Lynch. 1997. Adaptation and extinction in changing environments. Pages 209–39 in R. Bijlsma and V. Loeschcke, editors. Environmental Stress, Adaption and Evolution. Birkhauser Verlag, Basel, Switzerland.

Vincenzi, S. 2014. Extinction risk and eco-evolutionary dynamics in a variable environment with increasing frequency of extreme events. Journal of the Royal Society Interface 11:20140441.

Hill, W. G. 2010. Understanding and using quantitative genetic variation. Philosophical Transactions of the Royal Society of London. Series B, Biological sciences 365:73–85.

Johnson, T., and N. Barton. 2005. Theoretical models of selection and mutation on quantitative traits. Philosophical Transactions of the Royal Society of London. Series B, Biological sciences 360:1411–25.

Lynch, M., and R. Lande. 1993. Evolution and extinction in response to environmental change. Pages 234–250 in P. M. Kareiva, J. G. Kingsolver, and R. B. Huey, editors. Biotic Interactions and Global Change. Sinauer Associates, Sunderland, MA.