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snippets [2013/02/25 10:14] – [Approaches] mkopp | snippets [2019/03/21 09:21] (current) – external edit 127.0.0.1 | ||
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== Scope == | == Scope == | ||
- | * The topic of this special issue is " | + | * The topic of this special issue is " |
- | * In particular, current theory is unable to make quantitative predictions about the scope of phenotypic plasticity. In this paper, we will therefore ... | + | * In particular, current theory is unable to make quantitative predictions about the magnitude |
* Much of the relevant theory has not been developed with climate change specifically in mind (a notable exception being models of " | * Much of the relevant theory has not been developed with climate change specifically in mind (a notable exception being models of " | ||
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* Make qualitative predictions about the effect of different environmental and genetic variables. | * Make qualitative predictions about the effect of different environmental and genetic variables. | ||
* Provide rough rules of thumb for the magnitude of genetic (but not plastic, wee above) changes. | * Provide rough rules of thumb for the magnitude of genetic (but not plastic, wee above) changes. | ||
- | * Make suggestions about measurements and scaling (e.g. Herford | + | * Make suggestions about measurements and scaling (e.g. Hereford |
== Outline == | == Outline == | ||
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===== Genetic responses to climate change ===== | ===== Genetic responses to climate change ===== | ||
- | ==== Approaches | + | ==== Univariate case ==== |
- | === Modeling phenotypic evolution | + | === Sudden change |
- | == Quantitative genetics | + | === Gradual change === |
- | == Population genetics == | + | Consider now the case of gradual shift of a one-dimensional phenotypic optimum. Typically, a Gaussian fitness function is assumed: |
- | == Adaptive walks == | + | < |
+ | w(z) = \exp\left(-\frac{(z-\theta_t)^2}{2\sigma_s^2}\right) | ||
+ | </ | ||
- | == Adaptive dynamics == | + | Here, z is the phenotype of an individual, theta_t is the optimum at time t, and the " |
- | === Scenarios of environmental change === | + | < |
+ | \theta_t | ||
+ | </ | ||
- | == The baseline: Stabilizing selection == | + | where < |
- | == Sudden shift == | + | Assume that the trait z has a polygenic basis. In the simplest case (additive genetics, no plasticity), |
- | == Moving optimum == | + | < |
+ | \Delta \bar z = \sigma_g^2 \beta | ||
+ | </ | ||
- | == Incorporating stochasticity == | + | where the selection gradient < |
- | ==== Rates of evolution ==== | + | < |
+ | \beta = \frac{d \ln \bar w}{d \bar z}. | ||
+ | </ | ||
- | === Univariate case === | + | < |
+ | < | ||
+ | \beta = \frac{\bar z - \theta_t}{\sigma_s^2 + \sigma_e^2}, | ||
+ | </ | ||
+ | |||
+ | that is, the selection gradient increases linearly over time. As the optimum starts moving, an initially well-adapted population (z = \theta_0 = 0) will start to evolve, but initially, the selection gradient is small and, hence, the rate of adaptation is smaller than the rate of environmental change and the population will gradually slip off the optimum. However, as the distance to the optimum increases, so does the selection gradient, until finally a state of dynamic equilibrium is reached at which the rate of evolution exactly matches the rate of environmental change. At this equilibrium | ||
+ | |||
+ | < | ||
+ | \Delta \bar z^* = ... | ||
+ | </ | ||
+ | |||
+ | Even if the genetic variance is assumed to be constant, whether or not the population can actually follow the optimum depends on the population growth rate (mean fitness) at the equilibrium. The mean fitness can be decomposed into | ||
+ | |||
+ | < | ||
+ | \bar w_t = w_{max} - l_g - l_d | ||
+ | </ | ||
=== Multivariate case === | === Multivariate case === | ||
- | ==== Evolution and demography: Evolutionary rescue | ||
- | === Maximal sustainable rates? === | + | |
===== Plastic responses ===== | ===== Plastic responses ===== | ||
- | ==== Approaches ==== | + | ==== Approaches |
==== How much plasticity? ==== | ==== How much plasticity? ==== |