A comparison of methods to estimate abundance and biomass from belt transect surveys

Abstract

It is becoming increasingly popular to use continuously collected acoustic or optical data to estimate abundance or biomass of fish and invertebrates. However, data from such systems are typically highly spatially autocorrelated and zero‐inflated, and thus simple design‐based estimation techniques are not applicable. Model‐based estimation methods can be used to extrapolate observations along the observed track to larger areas. We tested the precision and accuracy of three model‐based methods using both simulations and field data: Ordinary kriging (OK), Generalized Additive Models with kriged model residuals (GAM + OK), and Generalized Additive Mixed Models with kriged model residuals (GAMM + OK), along with a design‐based method (stratified mean, SM). The GAMM + OK method treats small‐scale variations as random effects, whereas the other approaches aggregate nearby data to reduce autocorrelation and random errors. We found that the GAM + OK method with relatively small aggregation lengths generally gave the best performance of the model‐based methods in terms of both accuracy and precision, followed by GAMM + OK. SM estimates were more accurate and precise than the model‐based estimates in the simulations, but only when the study region was stratified accurately. Based on the simulation and field data analysis results, we selected the GAM + OK method to estimate scallop abundance and biomass for the Georges Bank and the Mid‐Atlantic Bight regions for the years 2011–2015. We also provided SM estimates based on careful stratifications to validate the model‐based estimates.

Bias Correction of Ocean Bottom Temperature and Salinity Simulations From a Regional Circulation Model Using Regression Kriging

“…In RK, the large-scale variation can be modeled using any type of regression which can be chosen based on the characteristics of the target simulated variables. For example, when the target variable is zero-inflated and over-dispersed, such as often the case for precipitation, two-stage hurdle regressions can be used (e.g., Chang et al, 2017;Zuur et al, 2009). A major advantage of RK is its ability to incorporate auxiliary information through covariates in the regression (Teutschbein & Seibert, 2012), so variables such as depth and month can be incorporated to inform the bias corrections.…”

“…RK is a spatial interpolation technique that models non-stationary trends using (generalized) regression, and then takes into account the spatial dependencies of the regression residuals using ordinary kriging (OK). RK has been used in a variety of disciplines and has proven effective in terms of modeling non-stationary and spatially autocorrelated objects (Chang et al, 2017;Hengl, 2009;Webster & Oliver, 2007). Unlike many bias correction methods that directly estimate the relationships between model-simulated output and observations, we use RK to estimate the "bias" across space and time.…”

Modelling the spatial population structure and distribution of the queen conch, Aliger gigas, on the Pedro Bank, Jamaica

“…The non-stratified method consisted of an estimate based on the product of the mean abundance and the total area and the weight conversion factors for adults and juveniles. Design method estimations can be significantly improved by stratification (Chang et al 2017), so we introduced stratification based on depth (0-10, 11-20 and 21-30 m). The second design biomass was then calculated similar to the first method but for each stratum and then summed to obtain total biomass.…”

“…From an ecosystem management standpoint, the risk of estimation errors is particularly problematic for density-dependent sedentary species such as the queen conch, in which allee effects can be enhanced by fishing leading to negative population growth and stock collapse (Stoner and Ray-Culp 2000). Chang et al (2017) suggested that predictions from design methods may be comparable to those from statistical models if the study area is accurately stratified. Accurate stratification can, however, be difficult or subjective and may contain large bias due to the unmodelled spatial autocorrelation from the spatial structure (Segurado et In this study, the best models were generally the GAMM and the ZINB based on the two evaluation statistics, MAE and RMSE.…”

Estimating natural mortality for Atlantic Sea scallops (Placopecten magellenicus) using a size-based stock assessment model

“…The models were fitted by maximum likelihood to abundance trends and size data obtained from sea scallop dredge surveys during 1975-2019 (Serchuk andWigley, 1986, Hart andRago, 2006), drop camera surveys conducted in most years since 2003 (Stokesbury et al, 2004, Bethoney andStokesbury 2018), towed camera (Habcam) surveys during 2011-2019 (Howland et al 2006, Chang et al 2017 as well as commercial landings and shell height data from port samples and at-sea observers. Model estimates of survey efficiency were constrained by priors (likelihood penalties); the priors from the two optical surveys had an expected efficiency of one, whereas the expected efficiency of the survey dredge was based on paired tow experiments with Habcam (Miller et al 2019).…”