Evolutionary Rescue in Structured Populations

Abstract

Environmental change, if severe, can drive a population extinct unless the population succeeds in adapting to the new conditions. How likely is a population to win the race between population decline and adaptive evolution? Assuming that environmental degradation progresses across a habitat, we analyze the impact of several ecological factors on the probability of evolutionary rescue. Specifically, we study the influence of population structure and density-dependent competition as well as the speed and severity of environmental change. We also determine the relative contribution of standing genetic variation and new mutations to evolutionary rescue. To describe population structure, we use a generalized island model, where islands are affected by environmental deterioration one after the other. Our analysis is based on the mathematical theory of time-inhomogeneous branching processes and complemented by computer simulations. We find that in the interplay of various, partially antagonistic effects, the probability of evolutionary rescue can show nontrivial and unexpected dependence on ecological characteristics. In particular, we generally observe a nonmonotonic dependence on the migration rate between islands. Counterintuitively, under some circumstances, evolutionary rescue can occur more readily in the face of harsher environmental shifts, because of the reduced competition experienced by mutant individuals. Similarly, rescue sometimes occurs more readily when the entire habitat degrades rapidly, rather than progressively over time, particularly when migration is high and competition strong.

Soft Selective Sweeps in Evolutionary Rescue

“…Our results regarding the probability of evolutionary rescue agree with results previously published using similar models of adaptation under de novo mutation variation (Orr and Unckless 2008, 2014; Uecker and Hermisson 2011; Martin et al 2013; Uecker et al 2014). …”

“…Note that this model is closely analogous to scenario 2 under “Alternative Forms of Population Regulation” in Orr and Unckless (2008), except that our model is in continuous time rather than discrete time. Our model is also similar to the $D=1$ panmictic model presented in Uecker et al (2014), except that we exclude the contribution of standing genetic variation and again look at continuous time vs. discrete time.…”

“…We explore a simple logistic model where fitness advantages are absolute and density dependent within the context of an evolutionary rescue scenario. While most models of evolutionary rescue predict the probability that rescue occurs (Gomulkiewicz and Holt 1995; Orr and Unckless 2008, 2014; Uecker and Hermisson 2011, 2016; Martin et al 2013; Uecker et al 2014), we focus on the likelihood that rescue occurs via hard or soft sweeps.…”

Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multidrug resistance

“…Second, we have assumed that treatment fails when the doubledrug compartment is invaded, but depending on the size and location of drug compartments in the body, treatment may fail when a single-drug compartment is invaded. Also, we assume a very specific migration model between the compartments [known in population genetics as the island model (56,57)], but other migration models may be possible. Specifically, not all compartments may be connected by migration and the migration rates may be independent of the size of the target compartment.…”

Is the sky the limit? On the expansion threshold of a species’ range

“…Haldane [12] 9 proposed a general explanation: even if environmental conditions vary smoothly, would lead to a sharp edge to a species' range, even if genetic variance at the range 13 margin is large. However, the consequences of dispersal and gene flow for evolution of a 14 species' range continue to be debated [13][14][15]: a number of studies suggest that 15 intermediate dispersal may be optimal [16][17][18][19][20]. Gene flow across heterogeneous 16 environments can be beneficial, because the increase of genetic variance allows the 17 population to adapt in response to selection [11].…”