Assessing the Influence of Temporal Autocorrelations on the Population Dynamics of a Disturbance Specialist Plant Population in a Random Environment

Abstract

Biological populations are strongly influenced by random variations in their environment, which are often autocorrelated in time. For disturbance specialist plant populations, the frequency and intensity of environmental stochasticity (via disturbances) can drive the qualitative nature of their population dynamics. In this article, we extended our earlier model to explore the effect of temporally autocorrelated disturbances on population persistence. In our earlier work, we only assumed disturbances were independent and identically distributed in time. We proved that the plant seed bank population converges in distribution, and we showed that the mean and variance in seed bank population size were both increasing functions of the autocorrelation coefficient for all parameter values considered, but the interplay between increasing population size and increasing variability caused interesting relationships between quasi-extinction probability and autocorrelation. For example, for populations with low seed survival, fecundity, and disturbance frequency, increasingly positive autocorrelated disturbances decreased quasi-extinction probability. Higher disturbance frequency coupled with low seed survival and fecundity caused a nonmontone relationship between autocorrelation and quasi-extinction, where increasingly positive autocorrelations eventually caused an increase in quasi-extinction probability. For higher seed survival, fecundity, and/or disturbance frequency, quasi-extinction probability was generally a monotonically increasing function of the autocorrelation coefficient.

Time‐lagged effects of weather on plant demography: drought and Astragalus scaphoides

“…Higher mean temperatures mean lower SPEI values, so the effect of mean temperature has the opposite sign to the SPEI effect. It would be informative to examine possible trade-offs between survival and flowering because life history trade-offs can lead to negative autocorrelation in time (Buckley et al 2010), which can have important implications for population persistence (e.g., Pike et al 2004, Tuljapurkar and Haridas 2006, Eager et al 2017.…”

Distributional ecology: Opening new research windows by addressing aggregation‐related puzzles

“…A distinct feature of temporal distributional aggregation is the irreversible order of the temporal data. Population dynamics of species are typically collected according to time sequences in which autocorrelations can be pervasive (Beckerman et al., 2002; Eager et al., 2017; Postuma et al., 2020). Notably, temporal distributional aggregation can be further divided into two categories: ecological‐time scale aggregation and evolutionary‐time scale aggregation, for which the former is usually shorter than the latter.…”

“…Notably, temporal distributional aggregation can be further divided into two categories: ecological‐time scale aggregation and evolutionary‐time scale aggregation, for which the former is usually shorter than the latter. For ecological‐time temporal aggregation, it might be understood as the grouping of some genetic characteristics in the pedigree analysis or the autocorrelated population growth dynamics, as mentioned previously (Beckerman et al., 2002; Eager et al., 2017; Postuma et al., 2020). For evolutionary‐time temporal aggregation, distributional aggregation may be best recognized as phylogenetic relatedness or proximity.…”