scholarly journals Population extinction by mutational load and demographic stochasticity

1993 ◽  
Vol 64 (2) ◽  
pp. 176
Author(s):  
Sean Nee

The study of metapopulation dynamics has had a profound impact on our understanding of how species relate to their habitats. A natural, if naïve, set of assumptions would be that species are to be found wherever there is suitable habitat that they can get to; that species will rarely, if ever, be found in unsuitable habitat; that they will be most abundant in their preferred habitat; that species can be preserved as long as a good-size chunk of suitable habitat is conserved for them; and that destruction of a species’ habitat is always detrimental for its abundance. We will see that none of these reasonable-sounding assumptions is necessarily true. Metapopulation biology is a vast field, so to focus this chapter I will be guided partly by questions relevant to conservation biology. There are two important kinds of metapopulation. The so-called Levins metapopulation idea (Levins, 1970) is illustrated in Figure 4.1. It is imagined that patches of habitat suitable for a species are distributed across a landscape. Over time, there is a dynamical process of colonization and extinction: the colonization of empty patches by occupied patches sending out colonizing propagules and the extinction of local populations on occupied patches. This extinction can occur for a number of reasons. Small populations are prone to extinction just by the chance vagaries of the environment, reproduction, and death—environmental and demographic stochasticity (May, 1974b; Lande et al., 2003). An example of a species for which this is important is the Glanville fritillary butterfly (Melitaea cinxia), which has been extensively studied by Hanski and colleagues (Hanski, 1999). This Scandinavian butterfly lives in dry meadows which are small and patchily distributed. Another reason for local population extinction is that the habitat patch itself may be ephemeral. For example, wood-rotting fungi will find that their patch ultimately rots completely away (Siitonen et al., 2005) and epiphytic mosses will ultimately find that their tree falls over (Snall et al., 2005). The second type of metapopulation consists of local populations connected by dispersal, but without the extinction of the local populations.


2019 ◽  
Vol 15 (2) ◽  
pp. e1006739 ◽  
Author(s):  
David V. McLeod ◽  
Troy Day

1986 ◽  
Vol 47 (1) ◽  
pp. 77-80 ◽  
Author(s):  
B. O. Bengtsson

SummaryRecombination is hard to understand in darwinian terms when the process is identified with the production of crossover chromosomes. As an alternative explanation I propose instead that biased conversion is the primary function of meiotic recombination. In particular I show that a conversion process directed against the most common type of genetic damage can substantially reduce the mutational load, even if the conversion force is weak and if the conversion process occasionally creates new mutations.


2007 ◽  
Vol 4 (16) ◽  
pp. 851-863 ◽  
Author(s):  
Alun L Lloyd ◽  
Ji Zhang ◽  
A.Morgan Root

Demographic stochasticity and heterogeneity in transmission of infection can affect the dynamics of host–vector disease systems in important ways. We discuss the use of analytic techniques to assess the impact of demographic stochasticity in both well-mixed and heterogeneous settings. Disease invasion probabilities can be calculated using branching process methodology. We review the use of this theory for host–vector infections and examine its use in the face of heterogeneous transmission. Situations in which there is a marked asymmetry in transmission between host and vector are seen to be of particular interest. For endemic infections, stochasticity leads to variation in prevalence about the endemic level. If these fluctuations are large enough, disease extinction can occur via endemic fade-out. We develop moment equations that quantify the impact of stochasticity, providing insight into the likelihood of stochastic extinction. We frame our discussion in terms of the simple Ross malaria model, but discuss extensions to more realistic host–vector models.


2008 ◽  
Vol 101 (26) ◽  
Author(s):  
Alex Kamenev ◽  
Baruch Meerson ◽  
Boris Shklovskii

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