Author Identifier

Ebenezer Afrifa-Yamoah
ORCID: https://orcid.org/0000-0003-1741-9249

Aiden Fisher
ORCID: https://orcid.org/0000-0002-5826-6800

Ute Mueller
ORCID: https://orcid.org/0000-0002-8670-2120

Document Type

Journal Article

Publication Title

ICES Journal of Marine Science

Publisher

Oxford Academic

School

School of Science

RAS ID

32435

Funders

Edith Cowan University - Open Access Support Scheme 2020

Comments

Afrifa-Yamoah, E., Taylor, S. M., Fisher, A., & Mueller, U. (2020). Imputation of missing data from time-lapse cameras used in recreational fishing surveys. ICES Journal of Marine Science, article fsaa180. https://doi.org/10.1093/icesjms/fsaa180

Abstract

While remote camera surveys have the potential to improve the accuracy of recreational fishing estimates, missing data are common and require robust analytical techniques to impute. Time-lapse cameras are being used in Western Australia to monitor recreational boating activities, but outages have occurred. Generalized linear mixed effect models formulated in a fully conditional specification multiple imputation framework were used to reconstruct missing data, with climatic and some temporal classifications as covariates. Using a complete 12-month camera record of hourly counts of recreational powerboat retrievals, data were simulated based on ten observed camera outage patterns, with a missing proportion of between 0.06 and 0.61. Nine models were evaluated, including Poisson and negative binomial models, and their associated zero-inflated variants. The imputed values were cross-validated against actual observations using percent bias, mean absolute error, root mean square error, and skill score as performance measures. In 90% of the cases, 95% confidence intervals for the total imputed estimates from at least one of the models contained the total actual counts. With no systematic trends in performance among the models, zero-inflated Poisson and its bootstrapping variant models consistently ranked among the top 3 models and possessed the narrowest confidence intervals. The robustness and generality of the imputation framework were demonstrated using other camera datasets with distinct characteristics. The results provide reliable estimates of the number of boat retrievals for subsequent estimates of fishing effort and provide time series data on boat-based activity.

DOI

10.1093/icesjms/fsaa180

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Research Themes

Natural and Built Environments

Priority Areas

Human-environment interaction

Share

 
COinS