Can the past predict the future? Experimental tests of historically based population models

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Journal/Book Title/Conference

Global Change Biology






Blackwell Publishing

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A frequently advocated approach for forecasting the population-level impacts of climate change is to project models based on historical, observational relationships between climate and demographic rates. Despite the potential pitfalls of this approach, few historically based population models have been experimentally validated. We conducted a precipitation manipulation experiment to test population models fit to observational data collected from the 1930s to the 1970s for six prairie forb species. We used the historical population models to predict experimental responses to the precipitation manipulations, and compared these predictions to ones generated by a statistical model fit directly to the experimental data. For three species, a sensitivity analysis of the effects of precipitation and grass cover on forb population growth showed consistent results for the historical population models and the contemporary statistical models. Furthermore, the historical population models predicted population growth rates in the experimental plots as well or better than the statistical models, ignoring variation explained by spatial random effects and local density-dependence. However, for the remaining three species, the sensitivity analyses showed that the historical and statistical models predicted opposite effects of precipitation on population growth, and the historical models were very poor predictors of experimental responses. For these species, historical observations were not well replicated in space, and for two of them the historical precipitation-demography correlations were weak. Our results highlight the strengths and weaknesses of observational and experimental approaches, and increase our confidence in extrapolating historical relationships to predict population responses to climate change, at least when the historical correlations are strong and based on well-replicated observations.