The effect of autocorrelation in environmental variability on the persistence of populations: an experimental test

Abstract

Despite its significance regarding the conservation and management of biological resources, the body of theory predicting that the correlation between successive environmental states can profoundly influence extinction has not been empirically validated. Identical clonal populations from a model experimental system based on the collembolan Folsomia candida were used in the present study to investigate the effect of environmental autocorrelation on time to extinction. Environmental variation was imposed by variable implementation (present/absent) of a culling procedure according to treatments that represented six patterns of environmental autocorrelation. The average number of culling events was held constant across treatments but, as environmental autocorrelation increased, longer runs of both favourable and unfavourable culling tended to occur. While no difference was found among the survival functions for the various treatments, the time taken for 50% of the component populations to become extinct decreased significantly with increasing environmental autocorrelation. Similarly, analysis of all extinct populations demonstrated that time to extinction was shortened as environmental autocorrelation increased. However, this acceleration of extinction can be fully offset if sequential introduction is used in place of simultaneous introduction when founding the populations.

Changes in temporal variance of rocky shore organism abundances in response to manipulation of mean intensity and temporal variability of aerial exposure

“…Note that different panels have different scales tality, algal overgrowth can strongly affect the survival of barnacles in the lowshore habitat in this system . This result was in agreement with theoretical modelling and empirical laboratory studies indicating that when fluctuations in environmental forcing variables are strongly autocorrelated in time or space, long-lasting negative conditions for species persistence become more likely and this can increase the risk of extinction of organisms (Heino 1998, Pike et al 2004). Alternatively, the large fluctuations in abundance of Chthamalus stellatus observed under reduced variability of aerial exposure might be determined by chance effects, like the coincidence of aerial exposure with the timing of recruitment of barnacles (between May and August on our shores, Benedetti-Cecchi et al 2000).…”

Community extinction patterns in coloured environments

“…In theoretical models and laboratory microcosms, population ER generally increases with increasing temporal autocorrelation (Halley 1996;Petchey et al 1997;Pike et al 2004;Schwager et al 2006). However, populations with different dynamical behaviour (over-or undercompensating) tend to react differently to increased temporal autocorrelation (Roughgarden 1975;Ripa & Lundberg 1996;Petchey et al 1997;Ripa & Heino 1999;Schwager et al 2006).…”

A review of extinction in experimental populations

“…Petchey (2000) found that Colpidium and Paramecium populations also tracked temperature fluctuations, leading to increased population variation. Finally, Pike et al (2004) directly controlled population size by culling and showed that autocorrelated culling decreased the time to extinction for springtails Folsomia candida.…”

“…These include: (1) determining how demographic stochasticity scales with population size (Drake 2006); (2) development of methods for identifying the optimal timing and magnitude of introductions to maximize colonization success (Pike et al 2004); (3) the long-term relative contributions of inbreeding depression, the loss of genetic diversity, and mutation accumulation to extinction risk (Frankham 2005); (4) the relative contributions of and interactions between genetic and non-genetic processes leading to extinction (Bijlsma et al 2000;Frankham 2005); (5) the genetic impacts of habitat fragmentation (Frankham 2005); (6) factors that influence outbreeding depression (Frankham 2005); (7) effects of non-random variable environments on population growth and decline (Drake 2006); (8) the interaction between environmental variation and migration on different time scales (Drake et al 2005;Long et al 2007); (9) the interaction between demographic and environmental stochasticity (Philippi et al 1987); and (10) the interaction between multiple limiting resources for population growth (Bancroft & Turchin 2003).…”

“…Autocorrelation in environmental variability (red noise) should increase population extinction (Lawton 1997). ** (Pike et al 2004) 13. Spatial variation in environmental quality should decouple dynamics of subpopulations and thereby decrease the risk of population extinction (Allen et al 1993;Earn et al 2000).…”