Robust prediction of population responses to changing environments requires the integration of factors controlling population dynamics with processes affecting distribution. This is true everywhere but especially in polar pelagic environments. Biological cycles for many polar species are synchronised to extreme seasonality, while their distributions may be influenced by both the prevailing oceanic circulation and sea-ice distribution. Antarctic krill (krill, Euphausia superba) is one such species exhibiting a complex life history that is finely tuned to the extreme seasonality of the Southern Ocean. Dependencies on the timing of optimal seasonal conditions has led to concerns over the effects of future climate on krill’s population status, particularly given the species’ important role within Southern Ocean ecosystems. Under a changing climate, established correlations between environment and species may breakdown. Developing the capacity for predicting krill responses to climate change therefore requires methods that can explicitly consider the interplay between life history, biological conditions, and transport. The Spatial Ecosystem And Population Dynamics Model (SEAPODYM) is one such framework that integrates population and general circulation modelling to simulate the spatial dynamics of key organisms. Here, we describe a modification to SEAPODYM, creating a novel model – KRILLPODYM – that generates spatially resolved estimates of krill biomass and demographics. This new model consists of three major components: (1) an age-structured population consisting of five key life stages, each with multiple age classes, which undergo age-dependent growth and mortality, (2) six key habitats that mediate the production of larvae and life stage survival, and (3) spatial dynamics driven by both the underlying circulation of ocean currents and advection of sea-ice. Here we present the first results of KRILLPODYM, using published deterministic functions of population processes and habitat suitability rules. Initialising from a non-informative uniform density across the Southern Ocean our model independently develops a circumpolar population distribution of krill that approximates observations. The model framework lends itself to applied experiments aimed at resolving key population parameters, life-stage specific habitat requirements, and dominant transport regimes, ultimately informing sustainable fishery management. This dataset represents KRILLPODYM modelled estimates of Antarctic krill circumpolar biomass distribution for the final year of a 12-year spin up. Biomass distributions are given for each of the five key life stages outlined above. The accompanying background, model framework and initialisation description can be found in the following reference paper: Green, D. B., Titaud, O., Bestley, S., Corney, S. P., Hindell, M. A., Trebilco, R., Conchon, A. and Lehodey, P. in review. KRILLPODYM: a mechanistic, spatially resolved model of Antarctic krill distribution and abundance. - Frontiers in Marine Science

These data represent the results of the first study to use Earth System Model (ESM) outputs of SST and chlorophyll-a to simulate circumpolar krill growth potential for the recent past (1960-1989) and future climate change scenarios (2070-2099). Growth potential is obtained using an empirically-derived krill growth model (Atkinson et al. 2006, Limnol. Oceanogr.), where growth is modeled as a function of SST and chlorophyll-a. It serves as an approximation of habitat quality, as areas that support high growth rates are assumed to be good habitat (see Murphy et al., 2017, Sci Rep). To increase confidence in the future projections, ESMs were selected and weighted for each season based on their skill at reproducing observation-based krill growth potential for the recent past. First, eleven ESMs which provided SST and chlorophyll-a outputs were obtained from the Coupled Model Inter-comparison Project 5 archive. These included: CanESM2, CMCC-CESM, CNRM-CM5, GFL-ESM2G, GFDL-ESM2M, GISS-E2-H-CC, HadGEM2-CC, IPSL-CM5A-LR, MPI-ESM-MR, MRI-ESM1 and NorESM1-ME. For each ESM, seasonal surface averages of SST and chlorophyll-a were used to calculate growth potential for the historical scenario (1960-1989), which was then bilinearly interpolated on to the same 1°x1° grid. Satellite observation-based datasets for SST and chlorophyll-a were used to calculate observation-based growth potential for the recent past (1997-2010). These comprised seasonal surface averages of SST (from the OISST v2 daily dataset, 1/4⁰ horizontal resolution) and chlorophyll-a (the mean of the SeaWiFS and Johnson et al. (2013) corrected estimate of SeaWiFS daily datasets, 1/12⁰ horizontal resolution). Observation-based growth potential was then bilinearly interpolated onto the same grid as the ESMs. ESM skill for each season was subsequently assessed against observation-based growth potential using a Taylor Diagram. The ESMs were selected and weighted according to their performance to produce a weighted subset (see "ESM_weighting_method.pdf" file). Of the netcdfs provided, "hist_mean_ensemble.nc" represents the unweighted mean of seasonal growth potential, calculated from the initial ensemble of eleven ESMs for the historical scenario. The "hist_mean_subset.nc" file represents the analogous output of the weighted subset. Future projections of seasonal growth potential for Representative Concentration Pathways (RCPs) 4.5 and 8.5 were obtained using the weighted subset for the period of 2070-2099. These projected seasonal surface averages are provided in the "rcp45_mean_subset.nc" and "rcp85_mean_subset.nc" files. RCPs represent standard climate change scenarios developed by the Intergovernmental Panel on Climate Change, with 4.5 reflecting some mitigation of carbon emissions, and 8.5 being the "business as usual" scenario. Analogous netcdfs for the weighted subset outputs of chlorophyll-a (chl) and SST (tos) for the historical and RCP scenarios are also provided in the "chl_tos_netcdfs.zip" file so that the driving environmental variables underlying growth potential can be examined.

Taken from the "Supporting Information" for the main paper. See the referenced papers for more information. Our results are based on numerical simulation of Southern Ocean sea ice, conducted using the Los Alamos numerical sea-ice model CICE version 4.0 [CICE4; Bailey et al., 2010] configured in stand-alone mode on a 0.25 degree x 0.25 degree grid, extending to 45 degrees S, with 3-hourly output [Stevens, 2013]. The atmospheric forcing for CICE4 came from the hemispheric forecasting model Polar Limited Area Prediction Systems [Polar- LAPS; Adams, 2006] and ocean forcing from the global ocean general circulation model Australian Climate Ocean Model [AusCOM; Bi and Marsland, 2010]. The model is well-constrained in its representation of processes of sea ice formation and melt, and comparison with observed areal ice extent shows minimal deviations over the 1998-2003 period, particularly during winter [Stevens 2013]. Stevens [2013] evaluates the sensitivity of the model to the number of ice thickness categories. Sea ice thickness sensitivities in the CICE model are considered in detail in Hunke [2010, 2014]. For the warm climate scenario, changes were implemented that are consistent with the A1B scenario from the Fourth Assessment from the IPCC [Meehl et al., 2007]. This is a mid-range scenario that assumes rapid economic growth before introduction of new and more efficient technologies mid century. Specifically, the following changes were applied uniformly to the current climate forcing field for a single year: a 2 degrees C increase in air temperature, a 0.2 mm/day increase in rain, a 1.5% increase in cloud fraction, a -2.3 hPa change in surface air pressure, a 25% increase in wind, a 12 Wm-2 increase in long wave downward radiation and a 20% increase in humidity. Outputs and forcings from CICE4 that are relevant for consideration of under-ice habitats for larval krill include: snow depth, ice thickness, ice concentration, movement, ridging rate, day length (dependent on day-of-year and latitude), radiation above the ice (influenced by cloud cover), and radiation below the ice (influenced by ice and snow depth). Table 1 in the main text describes how these were used in the following two filters and one overlay for evaluating the location and suitability of potential larval krill habitat during winter. Taken from the abstract of the main paper: Over-wintering of larvae underneath Antarctic pack ice is a critical stage in the life cycle of Antarctic krill. However, there are no circumpolar assessments of available habitat for larval krill, making it difficult to evaluate how climate change may impact this life stage. We use outputs from a circumpolar sea-ice model, together with a set of simple assumptions regarding key habitat features, to identify possible regions of larval krill habitat around Antarctica during winter. In particular we assume that the location and suitability of habitat is determined by both food availability and three dimensional complexity of the sea ice. We then compare the combined area of these regions under current conditions to that under a warm climate scenario. Results indicate that, while total areal sea-ice extent decreases, there is a consistently larger area of potential larval krill habitat under warm conditions. These findings highlight that decreases in sea-ice extent may not necessarily be detrimental for krill populations and underline the complexity of predicting future trajectories for this key species in the Antarctic ecosystem.

Antarctic krill (Euphausia superba) have a keystone role in the Southern Ocean, as the primary prey of Antarctic predators. Any decreases in krill abundance could result in a major ecological regime shift, but there is currently limited information on how climate change may affect krill. Increasing anthropogenic carbon dioxide (CO2) emissions are causing ocean acidification, as absorption of atmospheric CO2 in seawater alters ocean chemistry. Ocean acidification increases mortality and negatively affects physiological functioning in some marine invertebrates, and is predicted to occur most rapidly at high latitudes. Here we show that, in the laboratory, adult krill are able to survive, grow, store fat, mature, and maintain respiration rates when exposed to near-future ocean acidification (1000 – 2000 μatm pCO2) for one year. Despite differences in seawater pCO2 incubation conditions, adult krill are able to actively maintain the acid-base balance of their body fluids in near-future pCO2, which enhances their resilience to ocean acidification.

This restriction site associated DNA sequencing (RAD-seq) dataset for Antarctic krill (Euphausia superba) includes raw sequence data and summaries for 148 krill from 5 Southern Ocean sites. A detailed README.pdf file is provided to describe components of the dataset. DNA library preparation was carried out in two separate batches by Floragenex (Eugene, Oregon, USA). RAD fragment libraries (SbfI) were sequenced on an Illumina HiSeq 2000 using single-end 100 bp chemistry. As there is no reference genome for Antarctic krill, a set of unique 90 bp sequences (RAD tags) was assembled from 17.3 million single-end reads from an individual krill. We obtained over a billion raw reads from the 148 krill in our study (a mean of 6.8 million reads per sample). The reference assembly contained 239,441 distinct RAD tags. The core genotype dataset exported for downstream data filtering included just those SNPs with genotype calls in at least 80% of the krill samples and contained 12,114 SNPs on 816 RAD tags. Sample collection table (comma separated): Southern Ocean Location, Sample Size, Austral Summer, Latitude, Longitude, ID East Antarctica (Casey), 21, 2010/2011, 64S, 100E, Cas East Antarctica (Mawson), 22, 2011/2012. 66S, 70E, Maw Lazarev Sea, 38, 2004/2005 and 2007/2008, 66S, 0E, Laz Western Antarctic Peninsula, 16, 2010/2011, 69S, 76W, WAP Ross Sea, 23, 2012/2013, 68S, 178E, Ross

The embryonic development of Antarctic krill (Euphausia superba) is sensitive to elevated seawater CO2 levels. This data set provides the experimental data and WinBUGS code used to estimate hatch rates under experimental CO2 manipulation, as described by Kawaguchi et al. (2013). Kawaguchi S, Ishida A, King R, Raymond B, Waller N, Constable A, Nicol S, Wakita M, Ishimatsu A (2013) Risk maps for Antarctic krill under projected Southern Ocean acidification. Nature Climate Change (in press) Circumpolar pCO2 projection. To estimate oceanic pCO2 under the future CO2 elevated condition, we computed oceanic pCO2 using a three-dimensional ocean carbon cycle model developed for the Ocean Carbon-Cycle Model Intercomparison Project (2,3) and the projected atmospheric CO2 concentrations. The model used, referred to as the Institute for Global Change Research model in the Ocean Carbon-Cycle Model Intercomparison Project, was developed on the basis of that used in ref. 4 for the study of vertical fluxes of particulate organic matter and calcite. It is an offline carbon cycle model using physical variables such as advection and diffusion that are given by the general circulation model. The model was forced by the following four atmospheric CO2 emission scenarios and their extensions to year 2300. RCP8.5: high emission without any specific climate mitigation target; RCP6.0: medium-high emission; RCP 4.5: medium-low emission; and RCP 3.0-PD: low emission (1). Simulated perturbations in dissolved inorganic carbon relative to 1994 (the Global Ocean Data Analysis Project (GLODAP) reference year) were added to the modern dissolved inorganic carbon data in the GLODAP dataset (5). To estimate oceanic pCO2, temperature and salinity from the World Ocean Atlas data set (6) and alkalinity from the GLODAP data set were assumed to be constant. Marine ecosystems of the Southern Ocean are particularly vulnerable to ocean acidification. Antarctic krill (Euphausia superba; hereafter krill) is the key pelagic species of the region and its largest fishery resource. There is therefore concern about the combined effects of climate change, ocean acidification and an expanding fishery on krill and ultimately, their dependent predators—whales, seals and penguins. However, little is known about the sensitivity of krill to ocean acidification. Juvenile and adult krill are already exposed to variable seawater carbonate chemistry because they occupy a range of habitats and migrate both vertically and horizontally on a daily and seasonal basis. Moreover, krill eggs sink from the surface to hatch at 700–1,000m, where the carbon dioxide partial pressure (pCO2 ) in sea water is already greater than it is in the atmosphere. Krill eggs sink passively and so cannot avoid these conditions. Here we describe the sensitivity of krill egg hatch rates to increased CO2, and present a circumpolar risk map of krill hatching success under projected pCO2 levels. We find that important krill habitats of the Weddell Sea and the Haakon VII Sea to the east are likely to become high-risk areas for krill recruitment within a century. Furthermore, unless CO2 emissions are mitigated, the Southern Ocean krill population could collapse by 2300 with dire consequences for the entire ecosystem. The risk_maps folder contains the modelled risk maps for each of the climate change scenarios (i.e. Figure 4 in the main paper, and Figure S2 in the supplementary information). These are in ESRI gridded ASCII format, on a longitude-latitude grid with 1-degree resolution. Refs: 1. Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change 109, 213-241 (2011). Orr, J. C. et al. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437, 681-686 (2005). Cao, L. et al. The role of ocean transport in the uptake of anthropogenic CO2. Biogeosciences 6, 375-390 (2009). Yamanaka, Y. and Tajika, E. The role

This data may be used to reproduce the analyses (including figures and tables), of 'Two scales of distribution and biomass of Antarctic krill (Euphausia superba) in the eastern sector of the CCAMLR Division 58.4.2 (55°E to 80°E)'. The data describe krill biomass density distribution and krill net samples (krill total length and krill wetmass) collected during the 2021 TEMPO voyage on R/V Investigator. During the TEMPO voyage krill biomass was estimated using observations from two sampling instruments: a calibrated EK80 scientific echosounder operating at 120 kHz and an rectangular midwater trawl (RMT 1+8). The supporting data sets, all in CSV format, are split by instrument type. The EK80 has two datafiles: krill_density.csv – krill areal density from the TEMPO transects, and krill_swarms.csv -krill swarms detected during the TEMPO transects. The RMT1+8 has four datafiles net_locations.csv krill_lengths.csv krill_wet_mass_to_length.csv krill_wet_mass_to_length_model_predictions.csv The fields (columns) in each data file are: krill_density.csv "lat_M" – centre latitude of an echo integration interval [degrees] (dd.ddddd) WGS84 spheroid (GPS latitude) "lon_M" - centre longitude of an echo integration interval [degrees] (dd.ddddd) WGS84 spheroid (GPS longitude), "areal_biomass_density_g_per_m2" – Echo integration interval krill areal biomass density [g wet-mass / m^2] "daynight" – flag for when the sampling took place [day/night] "survey" – Either the main survey for the TEMPO biomass survey or the smaller-scale ‘Mawson box’ survey krill_swarms.csv "transect" – transect number "lat" – latitude [degrees] (dd.ddddd) WGS84 spheroid (GPS latitude) "swarm_depth_m" – mean depth of a krill swarm [m] "daynight" – flag for when the sampling took place [day/night] ”volumetric_density_g_per_m3" – krill swarm internal volumetric biomass density [g wet-mass / m^3] net_locations.csv "station" – Station name for net trawl R for routine haul, T for target trawl "lat" – mean latitude of a net trawl [degrees] (dd.ddddd) WGS84 spheroid (GPS latitude) "lon" – mean longitude of a net trawl [degrees] (dd.ddddd) WGS84 spheroid (GPS longitude) "daynight" – flag for when the sampling took place [day/night] krill_lengths.csv "station" – Station name for net trawl R for routine haul, T for target trawl "total_length_mm” – total length of an individual krill [mm] krill_wet_mass_to_length.csv "total_length_mm" – total length of an individual krill [mm] "wet_mass_g" - wet-mass an individual krill [g] krill_wet_mass_to_length_model_predictions.csv "total_length_mm" - total length of an individual krill [mm] "predicted_wet_mass_g" – predicted mean wet-mass an individual krill of length ("total_length_mm" ) [g] "LB_wet_mass_g" – Lower bound (lower 95% confidence interval) for the predicted_wet_mass_g [g] "UB_wet_mass_g"– Upper bound (upper 95% confidence interval) for the predicted_wet_mass_g [g]

Distribution and abundance of zooplankton, krill and fish were observed on the K-axis transect using deployments of RMT1+8 net. Towing speed of the RMT1+8 were approximately 2 knots. All krill, fish and squid in the catch were sorted, identified to species and counted. The density at each station were determined from the counts per calibrated flow-meter readings attached to the net. Morphometric measures were taken and, for larger taxa. List of files K-Axis Morph combined_for data centre.xlsx: Morphological data for all krill and zooplankton captured in RMT-8 net haul. RMT data entry_v1_for data centre.xlsx: Trawl data. RMT8 filtered volume_for data centre.xlsx: Filtered volume for each haul. Map_all.tif: Map showing all trawl stations. Map_RMTR.tif: Map showing only regular trawl stations. Map_RMTT.tif: Mapn showing only target trawl stations. K-Axis description This dataset includes biological data from “K-Axis voyage, 2016 and “Voyage 3, 2015”. [Data from K-Axis voyage, 2016] Distribution and abundance of zooplankton, krill and fish were observed on the K-axis transect using deployments of RMT1+8 net. Towing speed of the RMT1+8 were approximately 2 knots. All krill, fish and squid in the catch were sorted, identified to species and counted. The density at each station were determined from the counts per calibrated flow-meter readings attached to the net. Morphometric measures were taken and, for larger taxa. -List of files- K-Axis Morph combined_for data centre.xlsx: Morphological data for all krill and zooplankton captured in RMT-8 net haul. Map_all.tif Map_RMTR.tif Map_RMTT.tif RMT data entry_v1_for data centre.xlsx: Trawl data. RMT8 filtered volume_for data centre.xlsx: Filtered volume for each haul. [Data from Voyage 3, 2015] The Australian Antarctic research and resupply vessel, RV Aurora Australis, was directed to undertake an opportunistic marine science survey for 17 days during 21 February to 10 March 2015 using ship time that became available due to unexpectedly favourable ice conditions for Mawson station resupply. The purpose of this opportunistic Marine Science work was to assess: 1. The spatial variability, particularly along the shelf break, of the prey field for penguins, flying seabirds and marine mammals in East Antarctica. 2. The small scale variability of prey in key foraging locations near to land-based colonies of penguins and flying seabirds in East Antarctica. 3. Feasibility and potential of utilising annual station resupply voyages as a cost effective means to undertake monitoring and research to better understand the ecosystem in the region. The survey completed 5 acoustic box surveys including a total of 53 RMT target and routine trawls, 6 demersal trawls, 131 phytoplankton samples from underway sampling, and 214 hourly observations of predators. These activities were successfully supervised remotely. -List of files- emm-15-22.pdf: Prelminary report of the voyage to CCAMLR WG-EMM Figure_V3_all_euphausiids.pdf: Map of Euphausiid abundance distribution. Figure_V3_Clione_antarctica.pdf: Map of Clione antarctica abundance distribution. Figure_V3_crystal_krill.pdf: Map of Euphausia crystallorophias abundance distribution. Figure_V3_frigida.pdf: Map of Euphausia frigida abundance distribution. Figure_V3_larval_fish_abundances.pdf: Map of fish larvae abundance distribution. Figure_V3_superba.pdf: Map of Antarctic krill abundance distribution. Figure_V3_tmacrura.pdf: Map of Thysanoessa macrura abundance distribution. V3_final_for data centre.xlsx: Trawl station data and density data of each taxa caught. Voyage 3 Marine Science Program Final.docx: Voyage report.

This metadata record describes the 3-Dimensional coordinates of individual krill obtained from the ‘Swarm study’ at-sea deployment of stereo-cameras. An array of 10 Gopro Hero-8 cameras (2 cameras on each lateral side of a cubic frame, and a pair on the bottom) was deployed into a krill swarm during the 2021 TEMPO (Trends in Euphausiids off Mawson, Predators, and Oceanography) voyage onboard the RV Investigator (see Kelly et al. 2021 for more details on the voyage). The primary goal of TEMPO was to collect data to estimate krill biomass with a view to update the precautionary catch limit for krill in CCAMLR’s Division 58.4.2-East. The aim of the Swarm study was to replicate the methods of Burns et al., 2023, in order to examine the individual level behaviour of krill in swarms in natural conditions. The TEMPO voyage ran from February to March 2021. The Swarm study system was deployed when a krill swarm was identified from the ships echosounder and weather and sea conditions allowed for filming (i.e. minimal to no wind or swell, full sun). This data product contains coordinates of individual krill in three dimensions from a pair of stereo cameras. There are 2 datasheets: TEMPO_Orientation TEMPO_Tracks TEMPO_Orientation: this dataset contains the head and tail coordinate of 305 individual krill obtained from videos deployed into a krill swarm date: date video obtained deployment: deployment no. of swarm study panel: panel cameras attached to (for video ID) left/rightcam: for ID camera_dist: horizontal distance between the two cameras left/rightvid: video filename framediff: difference in frames between left and right camera still: still photo reference number that orientation data was obtained from frame (leftvid): corresponding frame for still pointref: point of krill where coordinate (head or tail) id: individual krill x1/y1: xy coordinates from left camera x2/y2: xy coordinates from right camera convertedXYZ: converted coordinates into 3Dimensional TEMPO_Tracks date: date video obtained deployment: deployment no. of swarm study panel: panel cameras attached to (for video ID) left/rightcam: for ID camera_dist: horizontal distance between the two cameras left/rightvid: video filename framediff: difference in frames between left and right camera fps: video frame rate per second clip: clip reference number frame: clip frame reference pointref: point of krill where coordinate (head or tail) x1/y1: xy coordinates from left camera x2/y2: xy coordinates from right camera on separate tabs: clip#: matched pairs of XY coordinates from the 2 cameras clip#XYZ: corresponding converted XYZ coordinates References Burns, A.L., Schaerf, T.M., Lizier, J., Kawaguchi, S., Cox, M., King, R., Krause, J. and Ward, A.J., 2022. Self-organization and information transfer in Antarctic krill swarms. Proceedings of the Royal Society B, 289(1969), p.20212361. Kelly, N., Bestley, S., Burns, A., Clarke, L., Collins, K., Cox, M., Hamer, D., King, R., Kitchener, J., Macaulay, G., Maschette, D., Melvin, J., Miller, B., Smith, A., Suter, L., Westwood, K., Wotherspoon, S. and Kawaguchi, S. (2021). An overview of the ecosystem survey to quantify krill abundance for krill monitoring and management in Eastern Sector of CCAMLR Division 58.4.2: Trends in Euphausiids off Mawson, Predators, and Oceanography “TEMPO”, Working Group on Ecosystem Monitoring and Management, CCAMLR, WG-EMM-2021/07, 26pp.

The objective of the study was to describe the spatial distribution of krill in eastern Canadian waters using a statistical modelling approach in support of the identification of important habitat for the western North Atlantic (WNA) blue whale (Balaenoptera musculus). Generalized Additive Models (GAMs) were used to predict ‘Significant Aggregations of Krill’ (SAK), i.e., areas where dense krill aggregations would have a greater probability of occurring. SAK cover less than 2% of the entire spatial domain and their location varied among krill categories and seasons. These SAK are interpreted as areas where environmental conditions promote krill aggregation on a regular basis and therefore are potentially important for WNA blue whale foraging in eastern Canadian waters. Plourde, S., Lehoux, C., McQuinn, I.H., and Lesage, V. 2016. Describing krill distribution in the western North Atlantic using statistical habitat models. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/111. v + 34 p.