Enhanced Moran effect by spatial variation in environmental autocorrelation

Abstract

Spatial correlations in environmental stochasticity can synchronize populations over wide areas, a phenomenon known as the Moran effect. The Moran effect has been confirmed in field, laboratory and theoretical investigations. Little is known, however, about the Moran effect in a common ecological case, when environmental variation is temporally autocorrelated and this autocorrelation varies spatially. Here we perform chemostat experiments to investigate the temporal response of independent phytoplankton populations to autocorrelated stochastic forcing. In contrast to naive expectation, two populations without direct coupling can be more strongly correlated than their environmental forcing (enhanced Moran effect), if the stochastic variations differ in their autocorrelation. Our experimental findings are in agreement with numerical simulations and analytical calculations. The enhanced Moran effect is robust to changes in population dynamics, noise spectra and different measures of correlation-suggesting that noise-induced synchrony may play a larger role for population dynamics than previously thought.

Temporal scale of environmental correlations affects ecological synchrony

“…), and phytoplankton (Massie et al . ). Two of these studies altered the spectral properties of the imposed environmental variation and analysed the effects on total population synchrony.…”

“…Controlled experimental studies involving laboratory microcosms can enhance our understanding of ecological phenomena (Jessup et al 2004;Desharnais 2005). Lab studies of synchrony have involved several species including soil mites (Benton et al 2001(Benton et al , 2002, rotifers and algae (Fontaine & Gonzalez 2005), Drosophila (Dey & Joshi 2006), maize weevils (Lima-Ribeiro et al 2007), bacteria and bacteriophages (Vogwill et al 2009), Euplotes and Tetrahymena predator-prey protists (Vasseur & Fox 2009;Fox et al 2011), ciliates and parasitic bacteria (Duncan et al 2015), and phytoplankton (Massie et al 2015). Two of these studies altered the spectral properties of the imposed environmental variation and analysed the effects on total population synchrony.…”

“…The authors showed that the synchronising effect of dispersal was enhanced in the populations with resource fluctuations biased towards long timescales. Massie et al (2015) varied the autocorrelation of the randomised dilution rates they used in their chemostat experiments and showed that synchrony between phytoplankton populations can, under some circumstances, be enhanced beyond the correlation of the environmental drivers, a phenomenon they called the 'enhanced Moran effect'.…”

Impact of environmental colored noise in single-species population dynamics

“…Remarkably, theoretical and empirical evidence reveals that these phenomena strongly rely on a specific interplay between the characteristic timescale of environmental variations and the intrinsic timescale of the dynamics [27][28][29][30][31]. Owing to this, theoretical approaches have to be constructed beyond simple white-noise approximations, i.e.…”

Demographic stochasticity and resource autocorrelation control biological invasions in heterogeneous landscapes

“…Local population dynamics in fluctuating and heterogeneous environments have been studied extensively in recent years (Duncan et al 2013;Gonzalez and Holt 2002), mainly with respect to population synchrony (Benton et al 2001;Fox et al 2011;Vasseur and Fox 2009). Both theoretical (Roy et al 2005;Vasseur 2007) and experimental (Fontaine and Gonzalez 2005;Gonzalez and Holt 2002;Massie et al 2015) studies have highlighted the relevance of the temporal autocorrelation structure of environmental fluctuations for ecological dynamics. The study of ecological processes in the presence of environmental stochasticity at different levels of autocorrelation is of interest not only because environmental fluctuations are typically positively correlated (Benincà et al 2011), but also in view of the global shift towards 'bluer' climate variables (i.e., more fluctuating) across most continents (e.g., García-Carreras and Reuman 2011).…”