UC Berkeley

Many short-lived species complete their life cycles during brief seasonal windows of favourable environmental conditions. Such species may persist in the face of climate warming by migration to track their seasonal climate niche in space and/or by phenological shifts to track favourable conditions in time within the year. To describe the seasonal climate niche of the short-lived annual Mollugo verticillata in California, we used data from herbarium specimens and historic climate records to estimate environmental conditions at the location, month and year of each collection. We used these data in a MaxEnt framework to construct a seasonal species distribution model (SDM) of the species’ climate niche within the total climate space available across all seasons and locations in California. The model provides fine-scale spatial and temporal predictions of habitat suitability, predicting both where and when the species should be observed. We compared the predictions of the model to those from a conventional SDM based on mean annual climate data. Both models showed that M. verticillata is limited to warm environments within California. However, the seasonal SDM also predicted phenology by mapping climate suitability across the state for each month of the year. Mollugo verticillata is limited to warm months, and its seasonal climate niche shifts in space across California in the course of the year. We used the seasonal SDM to map the predicted future species distribution for each month of the year under three warming scenarios. The species is predicted to expand its range and occur earlier in the year in most locations; in the warmest locations, seasonal suitability is predicted to decline in the warmest months, which may result in bimodal phenology with a mid-summer gap. Synthesis. We developed a novel species distribution model using herbarium records and monthly weather data, which predicts not only where a short-lived species should be found but also when during the year it is predicted to occur in those areas. This model can be used to predict how climate change will affect the species distribution in space as well as seasonal phenology across the landscape.