Demography and management of the invasive plant species Hypericum perforatum.: I.: Using multi-level mixed-effects models for characterizing growth, survival and fecundity in a long-term data set

1. Hypericum perforatum , St John's wort, is an invasive perennial herb that is especially problematic on waste ground, roadsides, pastures and open woodland in south-eastern Australia. We use detailed data from a long-term observational study to develop quantitative models of the factors affecting growth, survival and fecundity of H. perforatum individuals. 2. Multi-level or hierarchical mixed-effects statistical models are used to analyse how environmental and intrinsic plant variables affect growth and reproduction within a complex nested spatial and temporal context. These techniques are relatively underused in ecology, despite the prevalence of multi-level and repeated-measures data generated from ecological studies. 3. We found that plant size (rosette or flowering stems) was strongly correlated with all life stages studied (growth, probability of flowering, asexual reproduction, survival and fruit production). Environmental variables such as herbivory, ground cover and rainfall had significant effects on several life stages. 4. Significant spatial variation at the quadrat level was found in the probability of flowering, flowering stem growth and fruit production models; variation at all other spatial levels in all models was non-significant. Yearly temporal variation was significant in all models where multi-year data were available. 5. Plants in shaded habitats were smaller but had higher survival probabilities than plants in open habitats. They are therefore likely to have slightly different population dynamics. 6. Synthesis and applications. Analysis of these models for H. perforatum has provided insights into which plant traits and environmental factors determine how populations increase and persist in exotic ecosystems, enabling population management strategies to be most effectively targeted. Spatially and temporally correlated data are often collected in long-term ecological studies and multi-level models are a way in which we can fully exploit the wealth of data available. Without these tools data are either underexploited or crucial assumptions of independence on which many statistics are based are contravened.