Hidden in plain sight: Integrated population models to resolve partially observable latent population structure. Issue 12 (28th December 2022)

• Record Type: Journal Article
• Authors: Lawson, Abigail J.; Jodice, Patrick G. R.; Rainwater, Thomas R.; Dunham, Kylee D.; Hart, Morgan; Butfiloski, Joseph W.; Wilkinson, Philip M.; McFadden, K. W.; Moore, Clinton T.
• Is Part Of: Ecosphere. Volume 13:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-28
• Subjects: Alligator mississippiensis -- Bayesian -- harvest -- hierarchical model -- integrated population model -- population dynamics -- population structure -- South Carolina -- state uncertainty; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.4321
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Population models often require detailed information on sex‐, age‐, or size‐specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management decisions, which may be particularly problematic for stage‐ or size‐structured species subject to consumptive use. American alligators ( Alligator mississippiensis ; hereafter alligator) have a complex life history characterized by delayed maturity and slow somatic growth, which makes the species particularly sensitive to overharvest. Though alligator populations are subject to recreational harvest throughout their range, the most widely used monitoring method (nightlight surveys) is often unable to obtain size class‐specific counts, which limits the ability of managers to evaluate the effects of harvest policies. We constructed a Bayesian integrated population model (IPM) for alligators in Georgetown County, SC, USA, using records of mark–recapture–recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and somatic 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 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 harvest pressure. Here, we illustrate the use of IPMs to produce high‐resolution output of latent population structure that is partially observed during the monitoring process.