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Modelled mid-trophic pelagic prey fields improve understanding of marine predator foraging behaviour

Citation

Green, David et al. (2020), Modelled mid-trophic pelagic prey fields improve understanding of marine predator foraging behaviour, Dryad, Dataset, https://doi.org/10.5061/dryad.vhhmgqnqn

Abstract

Biophysical interactions are influential in determining the scale of key ecological processes within marine ecosystems. For oceanic predators, this means foraging behaviour is influenced by processes shaping the distribution of prey. However, oceanic prey is difficult to observe and its abundance and distribution is regionally generalised. We use a spatiotemporally resolved simulation model to describe mid-trophic prey distribution within the Southern Ocean and demonstrate insights that this modelled prey field provides into the foraging behaviour of a widely distributed marine predator, the southern elephant seal. From a five-year simulation of prey biomass, we computed climatologies of mean prey biomass (average prey conditions) and prey biomass variability (meso-scale variability). We also compiled spatially gridded metrics of seal density and diving behaviour from 13 years of tracking data. We statistically modelled these metrics as non-linear functions of prey biomass (both mean and variability) and used these to predict seal distribution and behaviour. Our predictions were consistent with observations (R2adj = 0.23), indicating that seals aggregate in regions of high mesoscale activity where eddies concentrate prey. Here, seals dived deeper (R2marg = 0.12, R2cond = 0.51) and spent less time hunting (R2marg = 0.05, R2cond = 0.56), likely targeting deep but profitable prey patches. Seals generally avoided areas of low eddy activity where prey was likely dispersed. Most seals foraged south of the Subantarctic Front, despite north of the front exhibiting consistently high simulated prey biomasses. This likely reflects seal prey or habitat preferences, but also emphasises the importance of mesoscale prey biomass variability relative to regionally high mean biomass. This work demonstrates the value of coupling mechanistic representations of prey biomass with predator observations to provide insight into how biophysical processes combine to shape species distributions. This will be increasingly important for the robust prediction of species’ responses to rapid system change.

Methods

Basin- and meso-scale metrics of prey distribution

Our study domain was the region south of 40 degrees (corresponding roughly to south of the Subtropical Front (STF)), where most elephant seal at-sea activity occurs. Within the Indian sector, female elephant seals dive to depths of, on average, 540 ± 178 m during the day and 402 ± 182 m at night (McMahon et al. 2019), which falls within the upper and lower mesopelagic depth bands (Fig. 1) (Proud et al. 2017, Trebilco et al. 2019). Therefore, we regarded the available prey field as including all those functional groups that are resident in or migrate through the both mesopelagic depth zones (Fig. 1): i.e. both migrant (2.2) and non-migrant upper mesopelagic (2.1) as well as the highly migrant (3.1) and migrant (3.2) and non-migrant (3.3) lower mesopelagic layers. Daily biomass of available prey (hereafter referred to as the available prey field) was then calculated by summing across these groups, and used to build spatial climatologies of prey distribution.

 

Two metrics were developed to represent the climatological distribution of prey at coarse and moderate (meso) temporal scales. Metric 1 – mean prey biomass: to represent average prey conditions, we calculated mean prey biomass across the full model period, effectively removing the short term variability in biomass associated with mesoscale activity. Metric 2 – prey biomass variability: to explicitly consider the role of mesoscale processes in aggregating prey, we calculated monthly variability of the daily output over the five years, thus giving us biomass variability. Mesoscale eddies serve to maximise foraging profits for seals (Dragon et al. 2010, Della Penna et al. 2015, Abrahms et al. 2018) by concentrating DSL biomass through entrainment and local enhancement, giving rise to distinct high-biomass patches particularly at the edges of eddies, with lower biomasses on either side (Sabarros et al. 2009). High levels of prey biomass variability would result from these prey patches being advected, by mesoscale processes such as eddies, through a given location over time. From an Eulerian perspective, this should give rise to high location-based prey biomass variation before, during and after passage of an eddy. The rate of this biomass variability should be roughly one month, equivalent to the average length-scaled displacement time estimated for ACC eddies (Park et al. 2002). It is also important to note that due to the chaotic nature of eddies (Pratt et al. 2014), circulation models are unlikely to forecast their exact location in space

and time. However, under conditions averaged over time, modelled and actual fields should provide reasonable spatial representation of areas (grid cells) where high mesoscale activity occurs. Consequently, we chose the mean monthly coefficient of variation (cv) of prey biomass per spatial grid cell across the five simulation years as a climatological representation of dynamic mesoscale prey distribution.

 

We focused upon the months of April–August to create our climatologies, to align with the foraging trips of seals (see below). During dispersal from the colony adult females may be driven more by intrinsic factors than by the availability of prey; we reduced the effect of these intrinsic factors by considering only the period when seals are most likely to be in their foraging areas. The two prey field climatologies were interpolated on to a 1° × 1° grid to directly relate to our marine predator usage metrics. A coarse grid would reduce resolution, but there is a necessary trade-off between coverage and resolution. We chose this resolution to minimise the number of empty cells, thereby ensuring adequate spatial coverage of our observational dataset.

 

Elephant seal tracking data

We used adult female southern elephant seals, which predominantly forage pelagically in the open ocean (Campagna et al. 1995, Bailleul et al. 2010a). From 2005 to 2018, 251 adult females were tagged at Kerguelen Island with Conductivity-Temperature-Depth Satellite Relay Data Loggers (CTDSRDL-9000 – Sea Mammal Research Unit, St Andrews, UK) prior to the onset of their post-moult migration (Roquet et al. 2014, Treasure et al. 2017). Full tagging details have been published elsewhere (Hindell et al. 2016). At-sea seal movements were determined through the ARGOS satellite tracking system (Roquet et al. 2014, Treasure et al. 2017). Due to the irregular timing and errors associated with ARGOS location data, these were filtered using a state-space model to obtain a regular 2 h time step of location estimates with reduced uncertainty (Jonsen et al. 2018). We only included females that foraged pelagically in deep (> 1000 m) waters as seals that forage over shelf regions (Kerguelen plateau or Antarctic shelf) predominantly perform benthic dives, likely targeting benthic prey rather than micronekton (O’Toole et al. 2014). We focused on oceanic foragers by retaining all individuals with at least 60% of their locations associated with waters deeper than 1000 m, based on estimates of bottom topography (ETOPO1 bathymetry, <www.ngdc.noaa.gov/mgg/global/>). Similarly, to eliminate any benthic diving that may have taken place along the shelf or slope, we excluded all dives with maximum depths within 20 m of the sea floor. Elephant seals are also known to perform various dives in which they do not actively search for prey (Dragon et al. 2012, Arce et al. 2019), so we removed all dives in which the seal recorded less than 60 s hunting time (see below for details). To exclude periods of dispersal during the beginning and end of the post-moult migration, we only considered tracks for which we had locational data from April through August. The final dataset consisted of 66 individual seals and tracks (Fig. 2b).

 

Metrics of predator distribution and foraging effort

We gridded (1° × 1° spatial resolution) seal location and dive data into metrics of distribution (seal density) and foraging effort. A satellite relayed data loggers (SRDL) transmits highly summarised dive information, reducing each dive profile to five segments delineated by the six main inflection points of the full profile (Heerah et al. 2015). For each dive, maximum dive depth (m), hunting time (s) and a dive residual (as defined below) were calculated. Hunting time, the duration of time spent in active prey search, was calculated following (Heerah et al. 2015). Hunting time is an index of vertical sinuosity within a dive to determine the duration spent in active foraging, indicated by those segments with a rate of change less than 0.4 m s−1. This method has been validated against accelerometer-inferred prey capture data, where segments classified as active hunting were associated with 68% of prey capture attempts, and outperformed similar metrics for inferring

hunting behaviour (Heerah et al. 2015). As dive depth increases, so too must the duration of descent and ascent. The dive residual – the residual of the dive depth versus dive duration regression (Bestley et al. 2015) – is a practical means of determining whether a dive is relatively long or short for a given depth (although see also Jouma’a et al. 2016). Comparisons between seal metrics and the modelled prey field were performed over the months April–August. Using our filtered dataset, we calculated a metric of seal distribution, defined as the number of seals visiting each grid cell over the

13 years of available data; and three metrics of foraging effort, defined as the mean maximum depth, hunting time and dive residual per individual per grid cell. The time-window of the simulation falls in the middle five years of the 13-yr tracking dataset and we use climatologies of the two periods to ensure that the two datasets can be analysed together meaningfully. All analyses were conducted using R 3.5.1 (R Core Team).

 

References

Abrahms, B., et al. 2018. Mesoscale activity facilitates energy gain in a top predator. - Proceedings of the Royal Society B: Biological Sciences 285: 20181101.

Arce, F., et al. 2019. A quantitative, hierarchical approach for detecting drift dives and tracking buoyancy changes in southern elephant seals. - Scientific Reports 9: doi: 10.1038/s41598-019-44970-1.

Bailleul, F., et al. 2010. Looking at the unseen: combining animal bio‐logging and stable isotopes to reveal a shift in the ecological niche of a deep diving predator. - Ecography 33: 709-719.

Bestley, S., et al. 2015. Taking animal tracking to new depths: synthesizing horizontal–vertical movement relationships for four marine predators. - Ecology 96: 417-427.

Campagna, C., et al. 1995. Diving behaviour and foraging location of female southern elephant seals from Patagonia. - Journal of Zoology 236: 55-71.

Della Penna, A., et al. 2015. Quasi-planktonic behavior of foraging top marine predators. - Scientific Reports 5: 18063.

Dragon, A.-C., et al. 2010. Linking foraging behaviour to physical oceanographic structures: Southern elephant seals and mesoscale eddies east of Kerguelen Islands. - Progress in Oceanography 87: 61-71.

Dragon, A., et al. 2012. Horizontal and vertical movements as predictors of foraging success in a marine predator. - Marine Ecology Progress Series 447: 243-257.

Heerah, K., et al. 2015. From high-resolution to low-resolution dive datasets: a new index to quantify the foraging effort of marine predators. - Animal Biotelemetry 3: doi 10.1186/s40317-015-0074-3.

Hindell, M. A., et al. 2016. Circumpolar habitat use in the southern elephant seal: implications for foraging success and population trajectories. - Ecosphere 7: e01213.

Jonsen, I., et al. 2018. Movement behaviour responses to environment: fast inference of individual variation with a mixed effects model. - bioRxiv 314690.

Jouma'a, J., et al. 2016. Adjustment of diving behaviour with prey encounters and body condition in a deep diving predator: the Southern Elephant Seal. - Functional Ecology 30: 636-648.

McMahon, C. R., et al. 2019. Finding mesopelagic prey in a changing Southern Ocean. - Scientific Reports 9: 1-11.

O’Toole, M., et al. 2014. Foraging behaviour of southern elephant seals over the Kerguelen Plateau. - Marine Ecology Progress Series 502: 281-294.

Park, Y.-H., et al. 2002. A quasi-synoptic view of the frontal circulation in the Crozet Basin during the Antares-4 cruise. - Deep Sea Research Part II: Topical Studies in Oceanography 49: 1823-1842.

Pratt, L. J., et al. 2014. Chaotic advection in a steady, three-dimensional, Ekman-driven eddy. - Journal of Fluid Mechanics 738: 143-183.

Proud, R., et al. 2017. Biogeography of the global ocean’s mesopelagic zone. - Current Biology 27: 113-119.

R Core Team 2018. R: A Language and Environment for Statistical Computing. - R Foundation for Statistical Computing.

Roquet, F., et al. 2014. A Southern Indian Ocean database of hydrographic profiles obtained with instrumented elephant seals. - Scientific Data 1: 140028.

Sabarros, P. S., et al. 2009. Mesoscale eddies influence distribution and aggregation patterns of micronekton in the Mozambique Channel. - Marine Ecology Progress Series 395: 101-107.

Treasure, A. M., et al. 2017. Marine Mammals Exploring the Oceans Pole to Pole: A review of the MEOP consortium. - Oceanography 30: 132-138.

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Usage Notes

Uploaded Datasets

The datasets uploaded are those used in model fitting and have already been processed following the above methods.

For a description of the above mentioned files, see attached README.txt

Full Datasets

Full datasets of the SEAPODYM-MTL model are available from the European COPERNICUS Marine Environment Monitoring Service (<http://marine.copernicus.eu/services-portfolio/access-to-products/>) and are not stored here.

The full Southern Elephant Seal tracking dataset from which our density and behavioural datasets are derived are available through the Integrated Marine Observing System (IMOS) portal (<https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/metadata.show>)

Funding

European Commission, Award: H2020 International Cooperation project MESOPP

University of Tasmania, Award: Tasmanian Graduate Research Scholarship

Institut Polaire Français Paul Emile Victor, Award: 1201

Australian Research Council, Award: DE180100828