The dark energy paradox leads to a new structure of spacetime.pptx
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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
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 215:689ā701.
34. Climate trend + extreme
events
ā¢ Focus on means (e.g. temperature)
ā¢ Different types of extreme events, e.g. depending on
timescale
ā Temperature, rainfall over season (climate extremes)
ā Floods, fires, hurricanes (point extremes)
ā¢ Adaptations are theoretically more likely for climate
extremes (generation time) ā ecological consequences
are similar, but not the evolutionary, e.g.
evolutionary rescue
ā¢ What happens when different extremes interact?
ā¢ Risk of extinction vortex
ā¢ We develop a (simulation) model
35.
36. Concepts/ingredients
ā¢ Genetic variance for quantitative trait
(adaptation to climate variable)
ā¢ Selection strength
ā¢ Mutation
--------------------------
ā¢ Directional trend
ā¢ Variability of climate variable
ā¢ Frequency and intensity of point
extremes
Biology
Environment
38. Climate variable (e.g. temperature)
tch = time of climate change
tinc = time of increasing
climate variance
Ī either the optimum phenotype
or a stochastic realization of climate
39. Point extremes
ā¢ Context-dependent
ā Frequency
ā Intensity (i.e. induced mortality)
ā¢ Cause same mortality risk to all individuals
ā¢ Mortality too high means system entirely
dominated by the point extremes
ā¢ 30% mortality after selection (after trial and
error)
41. Genetic variance
ā¢ VP = VA + VD + VI + VE
ā¢ h2 = VA/VP
ā¢ R = h2S ļ breederās 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
43. Model quantitative trait
ā¢ z = a + e
ā z = phenotype
ā a = breeding value (or genetic value)
ā e = environmental effect (from normal
distribution)
ā¢ a is determined by n (20 chosen) additive
genes
ā¢ Ļ of a is set as to have a defined heritability
for the trait (0.1-0.5)
ā¢ full recombination when mating
46. Simulations
ā¢ Simulations last 150 years after populations at
mutation-selection balance (500 individuals)
ā¢ Offspring at time t become adults and are able to
reproduce at time t + 1
ā¢ At the start of each simulation, for each individual a
value of a and e is randomly drawn from their initial
distribution.
ā¢ Sequence of operations: mortality of adults, mating
and reproduction, mutation, mortality of offspring.
ā¢ Population extinct if n <2
ā¢ Mating pairs are randomly drawn from the pool of
adults
ā¢ Each pair produces a number Pois(2) offspring
ā¢ Point extremes induce mortality after selection
51. How to analyze the results
ā¢ Drivers
(i) climate trend
(ii) climate variability
(iii) frequency of point extremes
(iv) selective pressure, genetic variability
and amplitude of mutation
ā¢ 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. ā¢ Simulations with combinations of parameters
values
ā¢ Parameter values chosen over a weak to strong
effect
ā¢ n replicates per combination (total 25 600)
ā¢ Specific hypotheses that limit the āresearcher
degrees of freedomā
ā¢ Standard statistical techniques (linear
regression, GLM)
ā¢ Effect size (and partial R2) and not statistical
significance to assess importance
How to analyze the results
53. Specific objectives/hypotheses
ā¢ 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
ā¢ 2 - greater mutation amplitude reduce the
risk of population extinction by increasing
genetic variability
58. Specific objectives/hypotheses
ā¢ 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
ā¢ The interaction among climate trend, variability
and probability of point extremes had minor effects
ā¢ Probability of occurrence of point extremes only
slightly increased risk of extinction
ā¢ Stronger selection and greater climate variability
increased extinction risk
ā¢ A simple model including four ecological, genetic
and demographic measures provided excellent
prediction of the immediate risk of population
extinction.
62. References
BĆ¼rger, R., and M. Lynch. 1995. Evolution and extinction in a changing
environment: a quantitative-genetic analysis. Evolution 49:151ā163.
BĆ¼rger, R., and M. Lynch. 1997. Adaptation and extinction in changing
environments. Pages 209ā39 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 365:73ā85.
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 360:1411ā25.
Lynch, M., and R. Lande. 1993. Evolution and extinction in response to
environmental change. Pages 234ā250 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.
SerotinyĀ is anĀ ecologicalĀ adaptation exhibited by someĀ 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, letās 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.
Letās have a look at another population. This population seemed to do fine too, butā¦
But we observed a collapse in this one too, two populations almost lost
Letās have a look at another population, also this one seemed to do ok, but at this point you might guess what happenedā¦
Boom, a collapse also in this population. Whatās 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. Letās 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
Letās go back to the case of marble trout. Whoās 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.