Using tagging data and aerial surveys to incorporate availability bias in the abundance estimation of blue sharks (Prionace glauca).

There is worldwide concern about the status of elasmobranchs, primarily as a result of overfishing and bycatch with subsequent ecosystem effects following the removal of top predators. Whilst abundant and wide-ranging, blue sharks (Prionace glauca) are the most heavily exploited shark species having suffered marked declines over the past decades, and there is a call for robust abundance estimates. In this study, we utilized depth data collected from two blue sharks using pop-up satellite archival tags, and modelled the proportion of time the sharks were swimming in the top 1-meter layer and could therefore be detected by observers conducting aerial surveys. The availability models indicated that the tagged sharks preferred surface waters whilst swimming over the continental shelf and during daytime, with a model-predicted average proportion of time spent at the surface of 0.633 (SD = 0.094) for on-shelf, and 0.136 (SD = 0.075) for off-shelf. These predicted values were then used to account for availability bias in abundance estimates for the species over a large area in the Northeast Atlantic, derived through distance sampling using aerial survey data collected in 2015 and 2016 and modelled with density surface models. Further, we compared abundance estimates corrected with model-predicted availability to uncorrected estimates and to estimates that incorporated the average time the sharks were available for detection. The mean abundance (number of individuals) corrected with modelled availability was 15,320 (CV = 0.28) in 2015 and 11,001 (CV = 0.27) in 2016. Depending on the year, these estimates were ~7 times higher compared to estimates without the bias correction, and ~3 times higher compared to the abundances corrected with average availability. When the survey area contains habitat heterogeneity that may affect surfacing patterns of animals, modelling animals' availability provides a robust alternative to correcting for availability bias and highlights the need for caution when applying "average" correction factors.

New insights into ocean sunfish (Mola mola) abundance and seasonal distribution in the northeast Atlantic.

The ocean sunfish, Mola mola, is the largest teleost fish in the world. Despite being found in all oceans of the world, little is known about its abundance and factors driving its distribution. In this study we provide the first abundance estimates for sunfish in offshore waters in the northeast Atlantic and the first record of extensive sunfish presence in these waters year-round. Abundance estimates and predictive distributions for sunfish in approximately 300,000 km² of the northeast Atlantic were derived from large scale offshore aerial surveys in 2015-2016 using distance sampling techniques. Generalized additive models of sunfish density were fitted to survey data from 17,360 km of line transect effort resulting in minimum abundance estimates of 12,702 (CI: 9,864-16,357) in the summer (Density = 0.043 ind/km²) and 8,223 individuals (CI: 6,178-10,946) (Density = 0.028 ind/km²) in the winter. Density surface models predicted seasonal shifts in distribution and highlighted the importance of the mixed layer depth, possibly related to thermoregulation following deep foraging dives. The abundance estimate and estimated daily consumption of 2,600 tonnes of jellyfish in the northeast Atlantic highlights the need to re-assess the importance of this species in the pelagic ecosystem, and its role in top-down control of jellyfish blooms.