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Vincenzi hopkins 2015
- 1. Simone Vincenzi EU Marie Curie Fellow University of California Santa Cruz, US Polytechnic of Milan, Italy simonevincenzi.com (publications) HMS, July 2015 Eco-evolutionary responses to extreme events
- 2. Ecology in the 21st century Past environments Evolutionary history Pheno traits Genetic variation Climate change Novel environment Individual fitness Evolution Population performance Population size Persistence Time
- 3. The world is becoming more extreme
- 4. 2014 NE US cold wave Akasped via Wikicommons Lake Michigan, Chicago
- 5. 2014 California drought Satellite images from NASA/NOAA
- 6. 2013-14 European floods Stefan Penninger via Wikicommons Passau, Germany
- 8. Extreme events are increasingly relevant Montpellier – September 2014 Parma – October 2014
- 9. Adaptations?
- 10. UK Department for International Development
- 11. FEMA Photo Library Daniel Mayer via Wikicommons
- 12. 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.
- 13. Example from my study system
- 16. risk 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha Gorska
- 17. risk 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha Gorska
- 18. risk 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha Lipovscek
- 19. risk 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha Lipovscek
- 20. risk 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha Zakojska
- 21. risk 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha Zakojska
- 24. Marble Slovenia 0 1000 2000 Rainfall (mm)
- 25. Extinct 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha ? 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha Safe 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha Zakojska Lipovscek Gorska
- 26. Extreme events cause population and genetic bottlenecks
- 27. Time risk 0 50 100 5 10 15 20 Year Populationsize
- 28. Population and genetic bottleneck Time risk 0 50 100 5 10 15 20 Year Populationsize
- 29. Time risk 0 50 100 5 10 15 20 Year Populationsize Extinct 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha
- 30. Time risk 0 50 100 5 10 15 20 Year Populationsize
- 31. Time risk 0 50 100 5 10 15 20 Year Populationsize Safe 0 500 1000 1500 '00 '04 '08 '12 Year Fish/ha
- 32. Time risk 0 50 100 5 10 15 20 Year Populationsize
- 33. Underreported
- 34. 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
- 36. 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
- 37. Environment
- 38. Climate variable (e.g. temperature) tch = time of climate change tinc = time of increasing climate variance Θ either the optimum phenotype or a stochastic realization of climate
- 39. 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)
- 40. Biology
- 41. 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 environment Al1 Al2 0 1 0 -1 2 1 2 0
- 42. Breeder’s equation and insights on the effects of selection
- 43. 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
- 44. Selection ω = width of fitness function
- 45. Selection Base mortality and effects of the climate variable
- 46. 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
- 47. Example
- 48. No variance no problem Lag
- 49. Examples
- 50. Examples
- 51. 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
- 52. • 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
- 53. 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
- 54. Risk of extinctionRisk of extinction Slow Fast
- 56. Shift of mean phenotype Increasing selection strength Fast mean change Slow mean change
- 57. Shift of mean phenotype
- 58. 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.
- 59. Predict extinction Predictors: 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
- 60. 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!
- 61. 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.
- 62. References Bürger, 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.
Editor's Notes
- Require long-term data collection, genomics and population genetics, population biology and some contribution from environmental science
- This past January an Artic cold front tracked across canada and the united states resulting in extreme low temperatures, heavy snowfall, schools, businesses, federal office closed and mass flight cancellations. Low temperature records were broken across the united states. In March more that 90% of Lake Michigan was frozen, a record reached only a few times in the last fifty years.
- Water crisis, drought emergency declared by Governor Brown. As you can see in the figures, quite a bit snow in 2013, no snow in 2014, it is pretty striking. California Central Valley is one of the most productive agricultural regions. The dry conditions cause also another problem. As soon as a storm arrives, as it happened a few days ago in Southern California, flash floods are more likely occur and the lack of vegetation caused by the drought and associated wildfires increases the risk of mudslides and loose rocks falling down. Despite the fact that there was snow in 2013, this is the third year of drought in California.
- In central Europe flooding occurred in Germany, Switzerland, Austria, Czech Republic, Slovenia. There were century floods and it was one of the worst european flooding since the middle ages Passau in Lower Bavaria, which sits right were the Danube, Inn and Ilz rivers meet experienced the worst flood in 500 years in June 2013. Some of these extreme events are occurring more frequently because of urbanization, negligent use of resources, for example cutting trees, or because infrastructures are built in vulnerable areas. Despite their rarity, extreme events do occur, but do we know of any adaptation present in species that give an advantage in case of extreme events?
- Spider Evasion
- Between 50% and 60% in US of the forest fires are started by humans, while the rest is caused by lightning.
- Serotiny is an ecological adaptation exhibited by some seed plants, in which seed release occurs in response to fire, rather than spontaneously at seed maturation. Different levels of cone serotiny have been linked to variations in the local fire regime: areas that experience more frequent crown-fire tend to have high rates of serotiny, while areas with infrequent crown-fire have low levels of serotiny.
- My model system is marble trout, a fish living only in freshwater. It survives a maximum of 10 years and it is closely related to the more popular brown trout ----- Meeting Notes (3/26/14 08:35) -----
- Ok, let’s have a look at how some of these populations were doing. The horizontal line is a threshold below which the population is at immediate risk of extinction. When I talk about extinction in this case I refer to local extinction or extirpation. This one seemed to do all right, but
- suddenly a few years ago we observed a collapse and the population went from a size safe to be close to extinction. That was kinda surprising.
- Let’s have a look at another population. This population seemed to do fine too, but…
- But we observed a collapse in this one too, two populations almost lost
- Let’s have a look at another population, also this one seemed to do ok, but at this point you might guess what happened…
- Boom, a collapse also in this population. What’s the reason behind these massive mortalities?
- It turned out that marble trout live in this mellow and peaceful streams that sometimes are not so peaceful and mellow
- Flash floods and debris flow occur in the area causing great damages to infrastructures, and also killing or displacing fish. Killing occurs since flash floods and debris flows move huge amounts of water along with rocks and boulders that sometimes smash the fish. Flash floods are characterized by time scales of less than a few hours, water goes up and down in a matter of hours and are an extreme event with catastrophic consequences for marble trout.
- Slovenia is the wettest nation in Europe and the small region where marble trout live receives more than two times the average rainfall of Slovenia. This combined with the topography of the region makes flash floods possible. At this point, you might want to know what was the fate of the 3 population that collapsed.
- One went extinct, the other bounced back to safe levels, and for the third population at this point we do not know if it is gonna make it or not. But why some populations are able to persist after a collapse and others not? It is just luck or it is dependent on some traits present in a population and not in another? These populations experience what is called in ecology and evolutionary biology a population bottleneck. Let’s have a look at a simplified illustration of a population bottleneck.
- This is an illustration of a population bottleneck. Each marble is an individual or a group of individuals in a population.
- At some point, due to environmental extreme events such as earthquakes, floods, fires and droughts there is a sharp reduction in population size, a population bottleneck. Just a few individuals are able to pass through, This event has demograhic consequences by reducing the number of individual alive and genetic consequences by basically reducing the genetic diversity of the population since just a fraction of the original genetic diversity is present after the collapse.
- If there are not enough individuals left, or the ones that survived are not able to reproduce sufficiently to re-form the species population, that population may go extinct.
- In other cases, the individuals passing through are the among the most fit individuals or have particular traits that help them reproduce successfully and re-form the populations
- And the population can thus bounce back to the pre-population bottleneck levels, as in the case of the orange population
- Let’s go back to the case of marble trout. Who’s passing through the bottleneck? Are the characteristics allowing the individuals to survive the extreme events, the same traits or traits correlated to those that help the surviving individuals reproduce successfully? But first we need to answer another question. How important are extreme events and the associated population bottlenecks for the risk of extinction of species? I think they are very important and they will become more important in the next years and decades.