Zulian, V, K Pacifici, NM Bacheler, JA Buckel, WF Patterson III, BJ Reich, KW Shertzer, NJ Hostetter. 2024. Applying mark-resight, count, and telemetry data to estimate effective sampling area and fish density with stationary underwater cameras. Canadian Journal of Fisheries and Aquatic Sciences. https://doi.org/10.1139/cjfas-2023-0373
Abstract
Accurate estimates of abundance and density within geographically open populations must account for sampling gear effective sampling area (ESA). We describe a Marked N-Mixture model to estimate ESA and density (number of individuals/unit area) from repeated counts of unmarked and marked individuals, integrating mark-resight, camera counts, and telemetry data of red snapper (Lutjanus campechanus) at a 1.6 km2 reef off North Carolina, USA. Cameras recorded observations of unmarked and marked individuals, whereas telemetry data indicated the number of tagged fish present on the reef. We estimated density (95 individuals/km2, 95%CI.:58–149), ESA (which was lower when current direction was towards the camera), detection probability (0.06, 95%CI.: 0.03–0.09), and covariate relationships. Simulation studies under different scenarios of data quality and space use identified positive bias in density estimates from N-mixture models due to fish movement, while the Marked N-Mixture model returned unbiased estimates of density and ESA. Our approach allowed the estimation of density, improved precision, and reduced the bias of parameter estimates, even under scenarios of poor data and animal movement, and can be applied to other geographically open populations where count and telemetry overlap in space and time.