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

Goliath grouper at a REEF diver site at the Aquarius Undersea Laboratory, near Plantation Key, Florida
TitleDemographic modeling of citizen science data informs habitat preferences and population dynamics of recovering fishes
Publication TypeJournal Article
Year of Publication2014
AuthorsThorson J.T, Scheuerell M.D, Semmens B.X, Pattengill-Semmens C.V
Date Published2014/12
Type of ArticleArticle
ISBN Number0012-9658
Accession NumberWOS:000346851400003
Keywordsbinned count data; challenges; citizen science; closure; counts; Dail-Madsen model; demographic; demographic variability; detectability; ecological research; estimating abundance; generalized linear mixed model (GLMM); habitat preferences; inferences; metapopulation; occupancy modeling; opportunistic data; site-structured model; state-space

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.


We have shown that site-structured demographic models can be combined with citizen science data obtained at large spatial and temporal scales and used to assess population trends, habitat effects, and inter-annual variability in production. In the case of Goliath grouper, these data demonstrate a dramatic increase in population abundance that is supported by independent data sets, i.e., fisher catch per unit effort from a Southern Florida creel survey (Cass-Calay and Schmidt 2009). In the case of mutton snapper, by contrast, available data demonstrate an approximately stable abundance since the early 1990s. Demographic models are likely to be a more efficient use of available data than generalized linear models (see de Valpine 2003), although the latter are frequently used to analyze survey data such as these.


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