scholarly journals The stochastic demography of two coexisting male morphs

Ecology ◽  
2011 ◽  
Vol 92 (3) ◽  
pp. 755-764 ◽  
Author(s):  
Isabel M. Smallegange ◽  
Tim Coulson
Keyword(s):  
Stochastic Demography

scholarly journals Elucidating fitness components of the invasive dermestid beetle Trogoderma granarium combining deterministic and stochastic demography

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0212182 ◽  
Author(s):  
Nikos E. Papanikolaou ◽  
Nickolas G. Kavallieratos ◽  
Marios Kondakis ◽  
Maria C. Boukouvala ◽  
Erifili P. Nika ◽  
...  
Keyword(s):  
Fitness Components ◽  
Trogoderma Granarium ◽  
Stochastic Demography

scholarly journals Variation in stochastic demography between and within central and peripheral regions in a widespread short-lived herb

Ecology ◽  
2013 ◽  
Vol 94 (6) ◽  
pp. 1378-1388 ◽  
Author(s):  
Jesús Villellas ◽  
William F. Morris ◽  
María B. García
Keyword(s):  
Stochastic Demography ◽  
Peripheral Regions

scholarly journals Evolution of stochastic demography with life history tradeoffs in density-dependent age-structured populations

2017 ◽  
Vol 114 (44) ◽  
pp. 11582-11590 ◽  
Author(s):  
Russell Lande ◽  
Steinar Engen ◽  
Bernt-Erik Sæther
Keyword(s):  
Life History ◽  
Population Growth ◽  
Density Dependence ◽  
Population Size ◽  
Structured Populations ◽  
Environmental Stochasticity ◽  
Trade Off ◽  
Density Dependent ◽  
Fluctuating Environment ◽  
Stochastic Demography

We analyze the stochastic demography and evolution of a density-dependent age- (or stage-) structured population in a fluctuating environment. A positive linear combination of age classes (e.g., weighted by body mass) is assumed to act as the single variable of population size, N, exerting density dependence on age-specific vital rates through an increasing function of population size. The environment fluctuates in a stationary distribution with no autocorrelation. We show by analysis and simulation of age structure, under assumptions often met by vertebrate populations, that the stochastic dynamics of population size can be accurately approximated by a univariate model governed by three key demographic parameters: the intrinsic rate of increase and carrying capacity in the average environment, r0 and K, and the environmental variance in population growth rate, σe2. Allowing these parameters to be genetically variable and to evolve, but assuming that a fourth parameter, θ, measuring the nonlinearity of density dependence, remains constant, the expected evolution maximizes E[Nθ]=[1−σe2/(2r0)]Kθ. This shows that the magnitude of environmental stochasticity governs the classical trade-off between selection for higher r0 versus higher K. However, selection also acts to decrease σe2, so the simple life-history trade-off between r- and K-selection may be obscured by additional trade-offs between them and σe2. Under the classical logistic model of population growth with linear density dependence (θ=1), life-history evolution in a fluctuating environment tends to maximize the average population size.

Unifying Ecological and Evolutionary Dynamics Through Experimental Stochastic Demography

2009 ◽  
Vol 55 (3) ◽  
pp. 199-205 ◽  
Author(s):  
Isabel M. Smallegange ◽  
Tim Coulson
Keyword(s):  
Evolutionary Dynamics ◽  
Evolutionary Ecology ◽  
Death Rates ◽  
Stochastic Environments ◽  
Push Forward ◽  
Demographic Methods ◽  
Stochastic Demography

Ecological and evolutionary dynamics depend upon variation in birth and death rates. Consequently characterizing birth and death rates, and identifying factors that explain variation in these rates, should be the foundation of population and evolutionary ecology. Given the central role of birth and death, it is perhaps surprising that relatively few population biologists apply the most recent demographic approaches to their research. This may be because demography is seen as little more than accounting, and therefore dull, or because stochastic demography is seen as mathematically challenging. It is our belief that ecologists and evolutionary biologists have much to gain through increased mastery of stochastic demography. Its applications could push forward our understanding of eco-evolutionary dynamics in stochastic environments, and the outcome could further the unification of ecology and evolution. In this essay we briefly explain why mastering demographic approaches should be a desirable objective for any evolutionary ecologist. We start by describing some aspects and insights gained through application of demographic methods, before suggesting an area where we believe application could prove insightful.

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