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Using the past to inform the future The late Quaternary paleorecord, within the past ∼130,000 years, can help to inform present-day management of the Earth's ecosystems and biota under climate change. Fordham et al. review when and where rapid climate transitions can be found in the paleoclimate record. They show how such events in Earth's history can shape our understanding of the consequences of future global warming, including rates of biodiversity loss, changes in ecosystem structure and function, and degradation in the goods and services that these ecosystems provide to humanity. They also highlight how recent developments at the intersection of paleoecology, paleoclimatology, and macroecology can provide opportunities to anticipate and manage the responses of species and ecosystems to changing climates in the Anthropocene. Science , this issue p. eabc5654

Population dynamics are typically temporally autocorrelated: population sizes are positively or negatively correlated with past population sizes. Previous studies have found that positive temporal autocorrelation increases the risk of extinction due to ‘inertia’ that prolongs downward fluctuations in population size. However, temporal autocorrelation has not yet been analyzed at the level of life cycle transitions. We developed an R package, colorednoise, which creates stochastic matrix population projections with distinct temporal autocorrelation values for each matrix element. We used it to analyze long-term demographic data on 25 populations from the COMADRE and COMPADRE databases and simulate their stochastic dynamics. We found a broad range of temporal autocorrelation across species, populations and life cycle stages. The number of stage-classes in the matrix strongly affected the temporal autocorrelation of the growth rate. In the plant populations, reproduction transitions had more negative temporal autocorrelation than survival transitions, and matrices dominated by positive temporal autocorrelation had higher extinction risk, while in animal populations transition type was not associated with noise color. Our results indicate that temporal autocorrelation varies across life cycle transitions, even among populations of the same species. We present the colorednoise package for researchers to analyze the temporal autocorrelation of structured demographic rates.