Statistical modelling of annual variation for inference on stochastic population dynamics using Integral Projection Models

Metcalf, C. Jessica E, Ellner, Stephen P, Childs, Dylan Z, Salguero-Gomez R., Merow, Cory, Mcmahon, Sean M, Jongejans, Eelke and Rees, Mark (2015) Statistical modelling of annual variation for inference on stochastic population dynamics using Integral Projection Models. Methods in Ecology and Evolution, 6 9: 1007-1017. doi:10.1111/2041-210X.12405

Author Metcalf, C. Jessica E
Ellner, Stephen P
Childs, Dylan Z
Salguero-Gomez R.
Merow, Cory
Mcmahon, Sean M
Jongejans, Eelke
Rees, Mark
Title Statistical modelling of annual variation for inference on stochastic population dynamics using Integral Projection Models
Journal name Methods in Ecology and Evolution   Check publisher's open access policy
ISSN 2041-210X
Publication date 2015
Year available 2015
Sub-type Article (original research)
DOI 10.1111/2041-210X.12405
Open Access Status Not Open Access
Volume 6
Issue 9
Start page 1007
End page 1017
Total pages 11
Place of publication Oxford, United Kingdom
Publisher Wiley-Blackwell Publishing
Collection year 2016
Language eng
Formatted abstract
1. Temporal fluctuations in vital rates such as survival, growth or reproduction alter long-term population dynamics and can change the dynamics from invasion and population persistence to extinction. Projections of population dynamics made in the absence of such fluctuations may consequently be misleading. However, data for estimation of yearly fluctuations in demographic parameters are often limited. Accordingly, the current diverse range of statistical and demographic modelling strategies used for stochastic population modelling may influence predictions.

2. We used simulations to explore the effects of different methods of parameter estimation on projections of population dynamics obtained using stochastic integral projection models (IPMs). The simulations were built from data on a monocarpic thistle, Carlina vulgaris, and an ungulate, Soay sheep, Ovis aries; these populations are subject to yearly fluctuation in vital rates facilitating the exploration of the effects of different methods of model construction on the properties of stochastic IPMs. Specifically, we looked at effects on the stochastic growth rate, log λs, and the mean and variance in the one-step population growth rate (Nt+1/Nt).

3. Our analyses showed that none of the tested approaches resulted in large biases in the estimation of log λs. However, when realistic study durations (e.g. 12 years) were used for statistical modelling, the confidence intervals around the λs estimates remained large. Estimation of the variance in one-step population growth rates, on the other hand, was strongly sensitive to the method employed, and the overestimation and underestimation of the variance were also influenced by the life history of the organism.

4. Our findings highlight the need to consider the influences of statistical and demographic modelling approaches when population dynamics have significant temporal stochasticity, as in population viability analyses and evolutionary predictions of bet hedging.
Keyword Covariation
Integral projection model
Population growth rate
Population projection
Population viability
Random vs. fixed effects
Sampling effects
Stochastic simulations
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Biological Sciences Publications
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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