Estimating Trans-Seasonal Variability in Water Column Biomass for a Highly Migratory, Deep Diving Predator

Abstract

The deployment of animal-borne electronic tags is revolutionizing our understanding of how pelagic species respond to their environment by providing in situ oceanographic information such as temperature, salinity, and light measurements. These tags, deployed on pelagic animals, provide data that can be used to study the ecological context of their foraging behaviour and surrounding environment. Satellite-derived measures of ocean colour reveal temporal and spatial variability of surface chlorophyll-a (a useful proxy for phytoplankton distribution). However, this information can be patchy in space and time resulting in poor correspondence with marine animal behaviour. Alternatively, light data collected by animal-borne tag sensors can be used to estimate chlorophyll-a distribution. Here, we use light level and depth data to generate a phytoplankton index that matches daily seal movements. Time-depth-light recorders (TDLRs) were deployed on 89 southern elephant seals (Mirounga leonina) over a period of 6 years (1999–2005). TDLR data were used to calculate integrated light attenuation of the top 250 m of the water column (LA250), which provided an index of phytoplankton density at the daily scale that was concurrent with the movement and behaviour of seals throughout their entire foraging trip. These index values were consistent with typical seasonal chl-a patterns as measured from 8-daySea-viewing Wide Field-of-view Sensor (SeaWiFs) images. The availability of data recorded by the TDLRs was far greater than concurrent remotely sensed chl-a at higher latitudes and during winter months. Improving the spatial and temporal availability of phytoplankton information concurrent with animal behaviour has ecological implications for understanding the movement of deep diving predators in relation to lower trophic levels in the Southern Ocean. Light attenuation profiles recorded by animal-borne electronic tags can be used more broadly and routinely to estimate lower trophic distribution at sea in relation to deep diving predator foraging behaviour.

Foraging strategy switch of a top marine predator according to seasonal resource differences

“…Random intercept models were then compared with random slope models (a random slope for PDI was added to the random slope models). Both seal and latitude were included as random terms in our analysis to allow for potential tag measurement variability and likely effect on phytoplankton abundance in the water column respectively (for details see O'Toole et al, 2014b). Second, we assessed the effect of inclusion of an autocorrelation term in the resulting optimal model by using the AR-1 autocorrelation (corAR1) argument.…”

“…Phytoplankton is consequently the main cause of light attenuation if it is assumed colored dissolved organic matter (CDOM) and detritus degradation products covary with phytoplankton (Bricaud et al, 1981) and physical properties are constant (Bricaud et al, 1998). Light data collected during daylight hours by marine animals can therefore provide a useful index for plankton density concurrent with animal movement (Teo et al, 2009;Guinet et al, 2013;O'Toole et al, 2014b) and have revealed seasonal trends typical of Southern Ocean productivity south of Iles Kerguelen and Macquarie Island (Jaud et al, 2012;O'Toole et al, 2014b).…”

Finding mesopelagic prey in a changing Southern Ocean

“…As prey density per se is not known to be influenced by water transparency, this metric provides strong support for the influential role of oceanographic features. Further, shading of the water column from the presence of phytoplankton and zooplankton can directly affect water transparency 47,72,73 . Mesopelagic prey are also likely more abundant in shallower waters when there is higher productivity (i.e.…”

View From Below: Inferring Behavior and Physiology of Southern Ocean Marine Predators From Dive Telemetry

“…A prime example is the improved knowledge of how elephant seals use specific water masses and oceanographic features obtained from high-quality temperature-salinity profiles collected onboard tags (e.g., Biuw et al, 2007;Labrousse et al, 2015;Hindell et al, 2016). Other novel approaches include the usage of onboard light-levels to infer bio-optical properties of the water column, including phytoplankton concentrations (Jaud et al, 2012;O'Toole et al, 2014), as well as direct fluorometry measurements to evaluate productivity influences on animal foraging. These clearly demonstrate the benefits gained from collecting environmental information onboard the same tag that is collecting the behavioral (dive) information.…”