Ability of Matrix Models to Explain the Past and Predict the Future of Plant Populations

ELIZABETH E. CRONE, MARTHA M. ELLIS, WILLIAM F. MORRIS, AMANDA STANLEY, TIMOTHY BELL, PAULETTE BIERZYCHUDEK, JOHAN EHRLÉN, THOMAS N. KAYE, TIFFANY M. KNIGHT, PETER LESICA, GERARD OOSTERMEIJER, PEDRO F. QUINTANA‐ASCENCIO, TAMARA TICKTIN, TERESA VALVERDE, JENNIFER L. WILLIAMS, DANIEL F. DOAK, RENGAIAN GANESAN, KATHYRN MCEACHERN, ANDREA S. THORPE, ERIC S. MENGES

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.