NSF Postdoctoral Fellowships in Biology combine research and training components to prepare young scientists for careers in emerging areas where biology intersects with other scientific disciplines, in this case with mathematics and statistics. The Fellows are expected to lead the nation?s scientific workforce of the future. This fellowship to Ailene K. Ettinger supports research and training to address plant performance by asking the ecological question, is it better to flower early as temperatures warm? It uses statistical tools to link timing with plant performance. The host institutions for this fellowship are Tufts University and Harvard University; the sponsoring scientists are Drs. Elizabeth Crone,Elizabeth Wolkovich, and Luke Bornn. Training goals include learning new modeling techniques, including the powerful statistical approach of Bayesian hierarchical modeling, through investigating the timing of many spring events, known as phenology, in some regions of the globe and the resulting effects on plants. Research results promise to assist natural resource managers in assessing whether phenology is a useful indicator for prioritizing species or populations that may be threatened by environmental change. They can determine if it is likely that a population of conservation or economic interest will decline in abundance if it has not shifted its flowering or leafing time with recent warming. Public outreach to conservation groups may interest them in conducting phenological monitoring on land they control.
In regions where temperatures have increased over the past century, phenology has shifted: numerous plant species are flowering or leafing earlier. This research applies Bayesian hierarchical models to datasets of environmental monitoring and experiments that document the extent to which leaf-burst, flowering, and/or fruiting dates have shifted with warming to determine how shifts in phenology affect plant performance and population growth rates. Bayesian hierarchical methods are rarely applied to meta-analyses in ecology, despite the advantages they offer for accommodating uncertainty, variance, data from multiple sources, and small datasets. Although increasingly used in ecological studies, they remain controversial but powerful when used appropriately.
