A 50-year (1971–2021) mesozooplankton biomass data collection in the Canary Current System: Base line, gaps, trends, and future prospect

Mesozooplankton have been widely used as a bioindicator of marine ecosystems due to their key position in ocean food webs, rapid response to environmental changes, and ubiquity. Here, we show mesozooplankton biomass values in the Canary Current System from 1971 to 2021 in three different areas in relation to mesoscale activity: (1) scarcely affected by mesoscales structures (North of the Canary Islands), (2) affected by mesoscale activity and the presence of the islands (South and around the islands), and (3) close to the Northwest African coastal upwelling system (Upwelling influenced). A Generalized Additive Mixed Model (GAMM) was used to analyze the general mesozooplankton biomass trend throughout the studied period discriminating differences in biomass between the areas, annual cycle, and day-nighttime periods. The GAMM showed a significant negative biomass tendency North of the Canary Islands over the 50-year time-series compared to the South and around the islands, and significant differences between day and nighttime periods (p < 0.001) and the annual cycle (p < 0.0001). Linear regression analyses showed different tendencies depending on the area, season, and period. When comparing biomass data of the most oligotrophic zone (north of the islands) with other tropical-subtropical time-series stations in Hawaii (HOTS) and Bermuda (BATS), we obtained increasing biomass tendencies for both fixed time stations but decreasing tendency for our time-series

Missing covariate data in generalized linear mixed models with distribution-free random effects

We consider generalized linear mixed models in which random effects are free of parametric distributions and missing at random data are present in some covariates. To overcome the problem of missing data, we propose two novel methods relying on auxiliary variables: a penalized conditional likelihood method when covariates are independent of random effects, and a two-step procedure consisting of a pairwise likelihood for estimating fixed effects in the first step and a penalized conditional likelihood for estimating random effects in the second step while covariates can be related to random effects. Our methods allow a nonparametric structure for the missing covariate data and do not rely on distribution assumptions for random effects, which are not observed in the data, thus providing great flexibility in capturing a board range of the missingness mechanism and behaviors of random effects. We show that the proposed estimators enjoy desirable theoretical properties by relaxing the conditions for a finite number of clusters or finite cluster size imposed in the literature. The finite sample performance of the estimators is assessed through extensive simulations. We illustrate the application of the methods using a longitudinal data set on forest health monitoring.MOE (Min. of Education, S’pore)Accepted versio