2024 article
Applying mark-resight, count, and telemetry data to estimate effective sampling area and fish density with stationary underwater cameras
Zulian, V., Pacifici, K., Bacheler, N. M., Buckel, J. A., Patterson Iii, W. F., Reich, B. J., … Hostetter, N. J. (2024, December 6). CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES.
author keywords: effective sampling area; mark-resight; N-mixture models; batch mark; red snapper; telemetry
UN Sustainable Development Goal Categories
14. Life Below Water
(OpenAlex)
Source: Web Of Science
Added: December 16, 2024
Accurate estimates of abundance and density for geographically open populations must account for the effective sampling area (ESA) of sampling gear. 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. In contrast, the Marked N-Mixture model returned unbiased estimates of density, ESA, and detection parameters, and appears to be a more robust method for modeling density given the data available for this analysis. This approach can be applied to other populations where count and telemetry data overlap in space and time.