Optimizing American Alligator Population Survey Design to Improve Management Decision-Making (10/19/2016)

*Presented October 19th, 2016 at the 23rd Annual Conference of The Wildlife Society*

ABSTRACT:

Robust species monitoring programs are the backbone of effective decision-making in wildlife population management. Reliability of monitoring data is heavily influenced by study design components, such as seasonal timing, and the number of sites or replicate surveys. Moreover, new monitoring programs are often modeled after established programs used in nearby areas or for closely related species, without a clear understanding of how differences in local conditions (e.g., environmental factors, habitat structure) may affect the precision and accuracy of demographic parameters estimated from monitoring data. Following 50 years of closure, the state of South Carolina re-opened American alligator (Alligator mississippiensis; hereafter alligator) populations to public lands harvest in 2008. The South Carolina Department of Natural Resources (SCDNR) is tasked with setting annual harvest regulations based on agency monitoring data. SCDNR’s fundamental objective is to maximize public lands alligator harvest opportunities, while maintaining a viable population, consisting of natural age and sex structure. South Carolina is near the northern extent of the alligator’s distribution, and hosts both diverse and highly fragmented habitat. Given South Carolina’s distinct habitat, our objective was to optimize the state’s nightlight survey monitoring program design, by maximizing the precision and accuracy of size class-specific abundance and detection probability estimates. We simulated an alligator population composed of five size classes, in which we implemented survey designs that varied by time of year, the number of sites and replicate surveys, and the size class assignment rate (i.e., the probability that an observed alligator was assigned to the correct size class), while using size-class specific detection probability estimates that were derived using field data. Here we will discuss the varying trade-offs in selecting an optimal monitoring design to produce reliable data for effective management decision-making.