Modelling community dynamics based on species-level abundance models from detection/nondetection data.
In large-scale field surveys, a binary recording of each species' detection or nondetection has been increasingly adopted for its simplicity and low cost. Because of the importance of abundance in many studies, it is desirable to obtain inferences about abundance at species-, functional group-, and community-levels from such binary data. We developed a novel hierarchical multi-species abundance model based on species-level detection/nondetection data. The model accounts for the existence of undetected species, and variability in abundance and detectability among species. Species-level detection/nondetection is linked to species-level abundance via a detection model that accommodates the expectation that probability of detection (at least one individuals is detected) increases with local abundance of the species. We applied this model to a 9-year dataset composed of the detection/nondetection of forest birds, at a single post-fire site (from 7 to 15 years after fire) in a montane area of central Japan. The model allocated undetected species into one of the predefined functional groups by assuming a prior distribution on individual group membership. The results suggest that 15-20 species were missed in each year, and that species richness of communities and functional groups did not change with post-fire forest succession. Overall abundance of birds and abundance of functional groups tended to increase over time, although only in the winter, while decreases in detectabilities were observed in several species. Synthesis and applications. Understanding and prediction of large-scale biodiversity dynamics partly hinge on how we can use data effectively. Our hierarchical model for detection/nondetection data estimates abundance in space/time at species-, functional group-, and community-levels while accounting for undetected individuals and species. It also permits comparison of multiple communities by many types of abundance-based diversity and similarity measures under imperfect detection.