Combining mesocosm and field experiments to predict invasive plant performance: a hierarchical Bayesian approach

摘要: Invasive plant fecundity underlies propagule pressure and ultimately range expansion. Predicting across large spatial scales, from regions to landscapes, is critical for understanding invasion dynamics optimizing management. However, accurately predict other demographic processes, improved models that scale individual responses abiotic drivers heterogeneous environments are needed. Here we combine two experimental data sets of a widespread problematic invasive grass over scales. First, analyzed seed production as function biomass in small-scale mesocosm experiment with manipulated light levels. Then, field introduction experiment, tracked performance 21 common garden sites differed widely available factors. We jointly these using Bayesian hierarchical model (BHM) framework the field. Our analysis reveals species likely produce sufficient overwhelm establishment resistance, even deeply shaded environments, seed-limited much its range. Finally, extend this address general problem how up processes analyze factors control distribution abundance at