Lawson, A.J., P.G.R. Jodice, T.R. Rainwater, K.D. Dunham, M. Hart, J.W. Butfiloski, P.M. Wilkinson, K.W. McFadden, C.T. Moore. Hidden in plain sight: integrated population models to resolve partially observable latent population structure. Ecosphere 13(12):e4321
State uncertainty of individuals within sampled populations is a ubiquitous problem in applied conservation, particularly for stage- or size-structured species subject to consumptive use. We constructed a Bayesian integrated population model (IPM) for American alligators (Alligator mississippiensis) in Georgetown County, South Carolina, USA using records of mark-recapture-recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and growth from other studies. We created a multistate mark-recapture-recovery model with six size classes to estimate survival probability, and we linked it to a state-space count model to derive estimates of size class-specific detection probability and abundance. Because we worked from a count dataset in which 60% of the original observations were of unknown size, we treated size class as a latent property of detections and developed a novel observation model to make use of information where size could be partly observed. Detection probability was positively associated with alligator size and water temperature, and negatively influenced by water level. Survival probability was lowest in the smallest size class (hatchlings) but was relatively similar among the other five size classes (>0.90 for each). While the two nightlight survey count sites exhibited relatively stable population trends, we detected substantially different patterns in size class-specific abundance and trends between each site, including 30–50% declines in the largest size classes at the site with greater hunting activity. Here, we illustrate the use of IPMs to produce high resolution output of latent population structure that is partially observed during the monitoring process.