Project at AIAS

Climate change is rapidly reshaping the biological world. One of the most prominent biological indicators of climate change is phenology – the timing of biological events – such as flowering. Phenology is intimately tied to the survival and reproduction of organisms, and thus is critical for their long-term persistence. Across the world, phenology is shifting earlier on average, but there is substantial variation in phenological responses across species and ecosystems. The overarching objective of my research is to provide increased mechanistic insight into the ecological and evolutionary factors that underlie this variation in species’ phenological responses to climate change. To address this, I will use two uniquely comprehensive datasets of flowering phenology and climate from snow-dominated ecosystems (Greenland and Colorado, USA), areas where the most extreme changes in climate and phenology are observed. In addition, records of long-term changes in floral abundance are available at one site, providing a measure of long-term performance of species and rare insight into the consequences of climate change. I will use novel analytical techniques to investigate key factors, and interactions among these factors, that are likely to underlie variation in flowering responses across species: (i) sensitivity of flowering to climate, (ii) plasticity in flowering time, and (iii) seasonality (spring vs. summer-flowering species). My research aims to create a more generalized framework of plant responses to climate change in snow-dominated ecosystems, which will help to predict these responses under future climate scenarios.

Short bio

Amy Iler earned her Ph.D. in evolution, ecology, and organismal biology from The Ohio State University, USA, in 2010. Following this, she accepted a postdoctoral research position at the University of Maryland and the Rocky Mountain Biological Laboratory, where she worked until starting her AIAS Fellowship in 2014. Amy’s research combines long-term data sets, field experiments, and demographic modeling to examine how global environmental change affects plant populations and plant-pollinator interactions.