Browsing by Author "Horn, Matthew"

Long term monitoring programs gather important data to understand population trends and man- age biodiversity, including phenological data. The sampling of such data can suffer from left- censoring where the first occurrence of an event coincides with the first sampling time. This can lead to overestimation of the timing of species’ life history events and obscure phenological trends. This study develops a Bayesian survival model to predict and impute the true first occurrence times of Finnish moths in a given sampling season in left-censored cases, thereby estimating the amount of left-censoring and effectively "decensoring" the data. A simulation study was done to test the model on synthetic data and explore how effect size, the severity of censoring, and sampling fre- quency effect the inference. Forward feature selection was done over environmental covariates for a generalized linear survival model with logit link, incorporating both left-censoring and interval censoring. Five-fold cross validation was done to select the best model and see what covariates would be added during the feature selection process. The validation tested the model both in its ability to predict points that were not left-censored and those that were artificially left-censored. The final model included terms for cumulative Growing Degree Days, cumulative Chilling Days, mean spring temperature, cumulative rainfall, and daily minimum temperature, in addition to an intercept term. It was trained on all of the data and predictions were made for the true first occurrence times of the left-censored sites and years.