Examining change over time in habitat attributes using Bayesian reinterpretation of categorical assessments

Prospects for evaluating effects of vegetation restoration have long been limited by availability of appropriately sensitive baseline data. Data that are typically collected to justify investment in restoration are rarely suitable for estimating subsequent change over time, but given how commonly such data are collected, can they contribute something to learning about ecological change over time? We compared vegetation and habitat data from a quantitative reassessment of 25 habitat restoration sites seven years after they were initially assessed using a semiquantitative, categorical scoring system. Our aim was to estimate the change at sites between the first, semiquantitative survey and a second, quantitative survey. We treated the initial values as effectively unknown and used Bayesian models to infer plausible values using three different informative prior distributions, variously comprising the initial site assessments and modeled values from a statewide data set. We successfully constructed models of change over time between the two surveys, and regardless of which prior model was implemented, our data analysis suggested that cover of exotic species was reduced, but canopy cover, the cover of organic litter, and the length of fallen logs were all increased after the seven-year period. A small increase in the mean number of large-diameter trees was likely due to initial measurement error. Site fertility and canopy cover were important covariates in explaining the magnitude of change in total log length. Sites with higher canopy cover decreased more in weed cover and increased more in litter cover. Our approach could be used to retrospectively analyze any ordinal data set where there is a scoring logic that can be interpreted quantitatively. Data sets where treatment contrasts and untreated controls exist will be particularly valuable for testing the utility of our approach. While this novel approach should prove a useful analytical complement to genuine longitudinal monitoring and space-for-time surveys, it is no substitute for initiation of learning about management effectiveness using data from purposefully designed and measured surveys.