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Temporal autocorrelation in environmental conditions influences population dynamics through its effects on vital rates. However, a comprehensive understanding of how and to what extent temporal autocorrelation shapes population dynamics is still lacking because most empirical studies have unrealistically assumed that environmental conditions are temporally independent. Mast seeding is a biological event characterized by highly fluctuating and synchronized seed production at the tree population scale as well as a marked negative temporal autocorrelation. In the current context of global change, mast seeding events are expected to become more frequent, leading to strengthened negative temporal autocorrelations and thereby amplified cyclicality in mast seeding dynamics. Theory predicts that population growth rates are maximized when the environmental cyclicality of consumer resources and their generation times are closely matched. To test this prediction, we took advantage of the long-term monitoring of a wild boar population, a widespread seed consumer species characterized by a short generation time (∼2 years). As expected, simulations indicated that its stochastic population growth rate increased as mast seeding dynamics became more negatively autocorrelated. Our findings demonstrate that accounting for temporal autocorrelations in environmental conditions relative to the generation time of the focal population is required, especially under conditions of global warming, where the cyclicality in resource dynamics is likely to change. [ABSTRACT FROM AUTHOR]

Objective: This article introduces Bayesian spatial–temporal modeling for social and health science research. We use the World Bank's World Development Indicators data on youth unemployment and HIV risk in Africa to illustrate the utility of the Bayesian paradigm in modeling space–time changes in outcomes. Method: Data on adolescents and young adults were collected in 36 African countries from 1991 to 2014. We examined associations between HIV risk and youth unemployment rates using 16 Bayesian Poisson models incorporating spatial and temporal autocorrelations. Results: The best fit to the data was the model with spatially uncorrelated heterogeneity, temporally correlated random-walk autocorrelation, and spatial–temporal interaction. HIV risk factors are spatially uncorrelated across 36 countries but temporally correlated (i.e., country and time interaction) over the data collection period. The relationship between HIV risk and unemployment rate is statistically nonsignificant because of large spatial–temporal variations. Conclusions: This article demonstrates the capacity of Bayesian modeling to incorporate spatial (neighborhood) and temporal (historical) information to reflect not only the influences of space and time but also their interactions on the phenomenon of interest. The Bayesian framework holds great promise for improving the dynamic targeting of interventions and strategies to achieve desired outcomes. [ABSTRACT FROM AUTHOR]

As global environmental conditions continue to change at an unprecedented rate, many species will experience increases in natural and anthropogenic stress. Generally speaking, selection is expected to favor adaptations that reduce the negative impact of environmental stress (i.e., stress tolerance). However, natural environmental variables typically fluctuate, exhibiting various degrees of temporal autocorrelation, known as environmental colors, which may complicate evolutionary responses to stress. Here we combine experiments and theory to show that temporal environmental autocorrelation can determine long-term evolutionary responses to stress without affecting the total amount of stress experienced over time. Experimental evolution of RNA virus lineages in differing environmental autocorrelation treatments agreed closely with predictions from our theoretical models that stress tolerance is favored in less autocorrelated (whiter) environments but disfavored in more autocorrelated (redder) environments. This is explained by an interaction between environmental autocorrelation and a phenotypic trade-off between stress tolerance and reproductive ability. The degree to which environmental autocorrelation influences evolutionary trajectories depends on the shape of this trade-off as well as the relative level of tolerance exhibited by novel mutants. These results suggest that long-term evolutionary dynamics depend not only on the overall strength of selection but also on the way that selection is distributed over time. [ABSTRACT FROM AUTHOR]