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Modelling time to population extinction when individual reproduction is autocorrelated. | LitMetric

AI Article Synopsis

  • Individual reproductive success in nature often varies due to factors like reproductive costs and differences between individuals, making it tricky to model this variability accurately.
  • The study compares a matrix population model that considers reproductive autocorrelations with a standard age-structured model, looking for biases in estimating population growth and extinction risk.
  • Results show that the two models yield similar extinction estimates (within 10%), suggesting that accounting for reproductive autocorrelations is usually unnecessary for accurate predictions.

Article Abstract

In nature, individual reproductive success is seldom independent from year to year, due to factors such as reproductive costs and individual heterogeneity. However, population projection models that incorporate temporal autocorrelations in individual reproduction can be difficult to parameterise, particularly when data are sparse. We therefore examine whether such models are necessary to avoid biased estimates of stochastic population growth and extinction risk, by comparing output from a matrix population model that incorporates reproductive autocorrelations to output from a standard age-structured matrix model that does not. We use a range of parameterisations, including a case study using moose data, treating probabilities of switching reproductive class as either fixed or fluctuating. Expected time to extinction from the two models is found to differ by only small amounts (under 10%) for most parameterisations, indicating that explicitly accounting for individual reproductive autocorrelations is in most cases not necessary to avoid bias in extinction estimates.

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Source
http://dx.doi.org/10.1111/ele.12834DOI Listing

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