Do we need demographic data to forecast population responses to 1 climate change ?

6 Rapid climate change has generated growing interest in forecasts of future population 7 trajectories. Traditional population models, typically built using detailed demographic 8 observations from one study site, can address climate change impacts at one location, but are 9 di cult to scale up to the landscape and regional scales relevant to management decisions. 10 An alternative is to build models using population-level data that is much easier to collect 11 over broad spatial scales than individual-level data. However, such models ignore the fact 12 that climate drives population growth through its influence on individual performance. Here, 13 we test the consequences of aggregating individual responses when forecasting climate change 14 impacts on four perennial grass species in a semi-arid grassland in Montana, USA. We 15 parameterized two population models based on the same dataset, one based on individual16 level data (survival, growth and recruitment) and one on population-level data (percent 17 cover), and compared their accuracy, precision, and sensitivity to climate variables. The 18 individual-level model was more accurate and precise than the aggregated model when 19 predicting out-of-sample observations. When comparing climate e ects from both models, 20 the population-level model missed important climate e ects from at least one vital rate for 21 each species. Increasing the sample size at the population-level would not necessarily reduce 22 forecast uncertainty; the way to reduce uncertainty is to capture unique climate dependence 23 of individual vital rates. Our analysis indicates that there is no shortcut to forecasting climate 24 1 E-mail: [email protected] 1 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/025742 doi: bioRxiv preprint first posted online Aug. 30, 2015;