Demographic modeling of citizen science data informs habitat preferences and population dynamics of recovering fishes

Abstract

Managing natural populations and communities requires detailed information regarding demographic processes at large spatial and temporal scales. This combination is challenging for both traditional scientific surveys, which often operate at localized scales, and recent citizen science designs, which often provide data with few auxiliary information (i.e., no information about individual age or condition). We therefore combine citizen science data at large scales with the demographic resolution afforded by recently developed, site-structured demographic models. We apply this approach to categorical data generated from citizen science representing species density of two managed reef fishes in the Gulf of Mexico, and use a modified Dail-Madsen model to estimate demographic trends, habitat associations, and interannual variability in recruitment. This approach identifies strong preferences for artificial structure for the recovering Goliath grouper, while revealing little evidence of either habitat associations or trends in abundance for mutton snapper. Results are also contrasted with a typical generalized linear mixed-model (GLMM) approach, using real-world and simulated data, to demonstrate the importance of accounting for the statistical complexities implied by spatially structured citizen science data. We conclude by discussing the increasing potential for synthesizing demographic models and citizen science data, and the management benefits that can be accrued.