Thirteen years of monitoring an alpine short-lived perennial: Novel methods disprove the former assessment of population viability

Dmitrii O. Logofet, Leonid L. Golubyatnikov, Elena S. Kazantseva, Iya N. Belova and Nina G. Ulanova

Ecological Modelling, 2023, vol. 477, issue C

Abstract: Our former assessment of viability in a local population of Androsace albana, an alpine short-lived perennial plant species (Logofet et al., 2020b), relied upon 10-year observations of the population structure on permanent sample plots and the corresponding, time-inhomogeneous, matrix model of stage-structured population dynamics. We applied a concept of the population in a random environment and its stochastic growth rate (λS) estimated by a Monte Carlo technique under the so-called “i.i.d.” mode of randomness, a simple model popular in the literature, meaning independent, identically distributed environments. All the Monte Carlo tests resulted in the estimates of λS < 1, meaning a negative forecast of population viability. A recent expansion of the monitoring period up to 13 years has now been combined with a more realistic model of randomness, namely, a Markov chain of changes in the environment that is recovered from a longer time-series of a local weather index correlated with variations in the asymptotic growth rate (λ1) of the model population within the 13-year period. The former design of Monte Carlo tests with the updated time series of λ1s and the realistic model of randomness have principally changed the viability assessment to λS > 1, whereas the i.i.d. tests have still resulted in λS < 1, i.e., a qualitatively opposite forecast of population viability.

Keywords: Discrete-structured population; Life cycle graph; Matrix calibration; Asymptotic growth rate; Pattern-geometric average; Stochastic growth rate; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:477:y:2023:i:c:s0304380022003064

DOI: 10.1016/j.ecolmodel.2022.110208

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