Modeling daily flowering probabilities: expected impact of climate change on Japanese cherry phenology.

Understanding the drivers of phenological events is vital for forecasting species' responses to climate change. We developed flexible Bayesian survival regression models to assess a 29-year, individual-level time series of flowering phenology from four taxa of Japanese cherry trees (Prunus spachiana, Prunus × yedoensis, Prunus jamasakura, and Prunus lannesiana), from the Tama Forest Cherry Preservation Garden in Hachioji, Japan. Our modeling framework used time-varying (chill and heat units) and time-invariant (slope, aspect, and elevation) factors. We found limited differences among taxa in sensitivity to chill, but earlier flowering taxa, such as P. spachiana, were more sensitive to heat than later flowering taxa, such as P. lannesiana. Using an ensemble of three downscaled regional climate models under the A1B emissions scenario, we projected shifts in flowering timing by 2100. Projections suggest that each taxa will flower about 30 days earlier on average by 2100 with 2-6 days greater uncertainty around the species mean flowering date. Dramatic shifts in the flowering times of cherry trees may have implications for economically important cultural festivals in Japan and East Asia. The survival models used here provide a mechanistic modeling approach and are broadly applicable to any time-to-event phenological data, such as plant leafing, bird arrival time, and insect emergence. The ability to explicitly quantify uncertainty, examine phenological responses on a fine time scale, and incorporate conditions leading up to an event may provide future insight into phenologically driven changes in carbon balance and ecological mismatches of plants and pollinators in natural populations and horticultural crops.

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes.

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated to the pregnant women's homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. The proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.

Assessing Habitat Use by Snapper (Chrysophrys auratus) from Baited Underwater Video Data in a Coastal Marine Park.

Baited Underwater Video (BUV) systems have become increasingly popular for assessing marine biodiversity. These systems provide video footage from which biologists can identify the individual fish species present. Here we explore the relevance of spatial dependence and marine park boundaries while estimating the distribution and habitat associations of the commercially and recreationally important snapper species Chrysophrys auratus in Moreton Bay Marine Park during a period when new Marine National Parks zoned as no-take or "green" areas (i.e., areas with no legal fishing) were introduced. BUV studies typically enforce a minimum distance among BUV sites, and then assume that observations from different sites are independent conditional on the measured covariates. In this study, we additionally incorporated the spatial dependence among BUV sites into the modelling framework. This modelling approach allowed us to test whether or not the incorporation of highly correlated environmental covariates or the geographic placement of BUV sites produced spatial dependence, which if unaccounted for could lead to model bias. We fitted Bayesian logistic models with and without spatial random effects to determine if the Marine National Park boundaries and available environmental covariates had an effect on snapper presence and habitat preference. Adding the spatial dependence component had little effect on the resulting model parameter estimates that emphasized positive association for particular coastal habitat types by snapper. Strong positive relationships between the presence of snapper and rock habitat, particularly rocky substrate composed of indurated freshwater sediments known as coffee rock, and kelp habitat reinforce the consideration of habitat availability in marine reserve design and the design of any associated monitoring programs.