PS 35-256 - Estimating spawning green sturgeon (Acipenser medirostris Ayres, 1854) abundance using side scan sonar and N-mixture models

Abstract: We investigated the use of side scan sonar and N-mixture models to improve the population estimate of the threatened southern distinct population segment of the North American Green Sturgeon (
Acipenser medirostris

). For three years from 2020 to 2022 we collected images of all known green sturgeon aggregations using a Humminbird model Helix 10 with a transducer transmitting at 1.2 megahertz, while simultaneously collecting data with a DIDSON or ARIS sonar. We compared a traditional plot sampling density estimator using the DIDSON and ARIS sonar data to an N-mixture model using side scan sonar images. We investigated the use of a number of different N-mixture model distributions including Binomial and Beta-Binomial for the observation process and Poisson, Negative Binomial and their zero inflated variants for the ecological abundance process as well as the use of covariates to explain the detection process. None of these standard models were either suitable for stable enough in the parameter estimates for our overdispersed data. However, an overdispersed Poisson model with normally distributed errors in both the abundance and observation processes fit the data. Our estimated abundances with this model are 742, 1286 and 1208 for 2020-2022 respectively. Compared to the traditional methods that gave estimates of 373 and 547 for 2020 and 2021, the population estimates increased 2-3 times, mostly from improved field methods. Because of the instability of the N-mixture models in terms of both parameter estimates and differences between analysis platforms, we ultimately would recommend much simpler population estimates that provide similar central tendencies (though with the drawback of no associated measures of variability about the estimates) so as to focus future energy on reducing variability in the data collection process.