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.

Integrating count and detection–nondetection data to model population dynamics

“…Population closure is not a reasonable assumption for some sampling protocols and integrating such data may involve adding alternative observation models including those that allow for false positives, double counting, or species misidentification (Miller et al 2011, Thorson et al 2014, Chambert et al 2016. Our results suggest that these efforts can provide accurate parameter estimates if the detection process is modeled correctly, but may still provide useful, if somewhat biased, estimates otherwise (Fig.…”

Release mortality of endangered Warsaw grouper Hyporthodus nigritus: a state-space model applied to capture-recapture data

“…Similar to Thorson et al (2014), our study used citizen-science data to better understand an endangered grouper species. Citizen-science data programs have become well recognized in ecological conservation as valuable complements to hypothesisdriven research and monitoring (Brewer 2002, Devictor et al 2010, Dickinson et al 2010.…”

Estimating transient populations of unmarked individuals at a migratory stopover site using generalized N‐mixture models

“…Since the generalized N-mixture model was introduced (Dail & Madsen, 2011), studies focused on developing population estimators that incorporate density dependence, intrinsic population growth rates, and environmental stochasticity into modelling (Bellier, Kéry, & Schaub, 2016;Hostetler & Chandler, 2015;Zipkin, Thorson, See, et al, 2014). Empirical application of generalized N-mixture models has been made to estimate population changes across generations (Bruggeman, Swem, Andersen, Kennedy, & Nigro, 2015;Hocking et al, 2013;McCaffery, Nowak, & Lukacs, 2016; Thorson, Scheuerell, Semmens, & Pattengill-Semmens, 2014;Zipkin, Sillett, Grant, et al, 2014), to test environmental effects on local abundances of an elusive bird species (Chandler & King, 2011), or to estimate the emergence timing of bivoltine butterflies (Matechou, Dennis, Freeman, & Brereton, 2014). To our knowledge, however, this approach has not been used to estimate a population size that is subject to extreme temporal changes, as occurring in migratory populations at staging sites.…”