Site‐Occupancy Distribution Modeling to Correct Population‐Trend Estimates Derived from Opportunistic Observations

Abstract: Species’ assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection–nondetection records) are generated. Within‐season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site‐occupancy models are applied directly to the detection‐history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site‐occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen‐science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.     

Predicting Offenses and Crashes from Young Drivers' Offense and Crash Histories

Previous research has indicated that offenses are better predictors of subsequent crashes than crashes themselves. We examined this hypothesis for 13,800 young beginning drivers in Michigan for up to nine years during the initial years of driving. Our analyses indicated that previous-year offenses are better predictors of both subsequent-year offenses and crashes than either previous-year crashes or at-fault crashes. This finding also held for the apparently higher-risk subset of subsequent-year serious offenses and at-fault crashes. Although there were no gender differences in the predictive power of crashes, it was found that the predictive power of previous offenses to subsequent serious offenses was significantly stronger for women than for men. The predictive power of incidents appeared to increase somewhat with increasing driving experience, suggesting that early incidents may be more attributable to inexperience, a characteristic of all beginning drivers, while later incidents may be more attributable to individual differences.