Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 7452
Title: Demographic modeling of citizen science data informs habitat preferences and population dynamics of recovering fishes
Author: James T. Thorson, M. D. Scheuerell, B. X. Semmens, Christy Pattengill-Semmens
Publication Year: 2014
Journal: Ecology
Volume: 95
Issue: 12
Pages: 3251-3258
Keywords: Dail-Madsen model

 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 offer little demographic resolution.  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 representing species density generated via citizen science 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 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 modern population models and citizen science data, and the management benefits that can be accrued.


Theme: Recovery and rebuilding of marine and coastal species
Foci: Develop methods to use physiological, biological and behavioral information to predict population-level processes.
Characterize the population biology of species, and develop and improve methods for predicting the status of populations.