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Extinction Time (extinction + time)

Distribution by Scientific Domains
Life Sciences100%

Selected Abstracts

Demographic factors and genetic variation influence population persistence under environmental change

JOURNAL OF EVOLUTIONARY BIOLOGY, Issue 1 2009
YVONNE WILLI
Abstract Population persistence has been studied in a conservation context to predict the fate of small or declining populations. Persistence models have explored effects on extinction of random demographic and environmental fluctuations, but in the face of directional environmental change they should also integrate factors affecting whether a population can adapt. Here, we examine the population-size dependence of demographic and genetic factors and their likely contributions to extinction time under scenarios of environmental change. Parameter estimates were derived from experimental populations of the rainforest species, Drosophila birchii, held in the lab for 10 generations at census sizes of 20, 100 and 1000, and later exposed to five generations of heat-knockdown selection. Under a model of directional change in the thermal environment, rapid extinction of populations of size 20 was caused by a combination of low growth rate (r) and high stochasticity in r. Populations of 100 had significantly higher reproductive output, lower stochasticity in r and more additive genetic variance (VA) than populations of 20, but they were predicted to persist less well than the largest size class. Even populations of 1000 persisted only a few hundred generations under realistic estimates of environmental change because of low VA for heat-knockdown resistance. The experimental results document population-size dependence of demographic and adaptability factors. The simulations illustrate a threshold influence of demographic factors on population persistence, while genetic variance has a more elastic impact on persistence under environmental change. [source]

The effects of habitat destruction in finite landscapes: a chain-binomial metapopulation model

OIKOS, Issue 2 2001
Mark F. Hill
We present a stochastic model for metapopulations in landscapes with a finite but arbitrary number of patches. The model, similar in form to the chain-binomial epidemic models, is an absorbing Markov chain that describes changes in the number of occupied patches as a sequence of binomial probabilities. It predicts the quasi-equilibrium distribution of occupied patches, the expected extinction time ( ), and the probability of persistence ( ) to time x as a function of the number N of patches in the landscape and the number S of those patches that are suitable for the population. For a given value of N, the model shows that: (1) and are highly sensitive to changes in S and (2) there is a threshold value of S at which declines abruptly from extremely large to very small values. We also describe a statistical method for estimating model parameters from time series data in order to evaluate metapopulation viability in real landscapes. An example is presented using published data on the Glanville fritillary butterfly, Meltiaea cinxia, and its specialist parasitoid Cotesia melitaearum. We calculate the expected extinction time of M. cinxia as a function of the frequency of parasite outbreaks, and are able to predict the minimum number of years between outbreaks required to ensure long-term persistence of M. cinxia. The chain-binomial model provides a simple but powerful method for assessing the effects of human and natural disturbances on extinction times and persistence probabilities in finite landscapes. [source]

Which traits promote persistence of feral GM crops?

OIKOS, Issue 1 2005
Part 1:implications of environmental stochasticity
Transgenes in plants affect life history traits including seed survival and germination. With stochastic matrix models we predict population-level consequences of transgene induced life history changes. We assess systematically which changes in life history traits, resulting from genetic modification, may increase the risk of invasion and persistence of feral crops or increase fitness in case of introgression from arable fields into conspecific, feral populations. We apply our method to feral populations of oilseed rape. Like many annual weeds, oilseed rape depends critically on disturbance; in undisturbed habitats it is generally outcompeted by perennials. The associated inherent variability and unpredictability render deterministic models inappropriate. With a stochastic matrix model we study population growth rate, elasticities and quasi-extinction times. Our results indicate that changes in survival in the seed bank impact population growth and persistence most. Less important are dormancy, fecundity and seedling survival. The predicted distribution of extinction times is highly skewed, with some patches persisting for decades. [source]

The effects of habitat destruction in finite landscapes: a chain-binomial metapopulation model

OIKOS, Issue 2 2001
Mark F. Hill
We present a stochastic model for metapopulations in landscapes with a finite but arbitrary number of patches. The model, similar in form to the chain-binomial epidemic models, is an absorbing Markov chain that describes changes in the number of occupied patches as a sequence of binomial probabilities. It predicts the quasi-equilibrium distribution of occupied patches, the expected extinction time ( ), and the probability of persistence ( ) to time x as a function of the number N of patches in the landscape and the number S of those patches that are suitable for the population. For a given value of N, the model shows that: (1) and are highly sensitive to changes in S and (2) there is a threshold value of S at which declines abruptly from extremely large to very small values. We also describe a statistical method for estimating model parameters from time series data in order to evaluate metapopulation viability in real landscapes. An example is presented using published data on the Glanville fritillary butterfly, Meltiaea cinxia, and its specialist parasitoid Cotesia melitaearum. We calculate the expected extinction time of M. cinxia as a function of the frequency of parasite outbreaks, and are able to predict the minimum number of years between outbreaks required to ensure long-term persistence of M. cinxia. The chain-binomial model provides a simple but powerful method for assessing the effects of human and natural disturbances on extinction times and persistence probabilities in finite landscapes. [source]