Code, data and results used to fit growth rates of Antarctic krill under experimental CO2 manipulation
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...
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Australian Ocean Data Network
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| Online Access: | https://researchdata.ands.org.au/code-results-used-co2-manipulation/687361 https://data.aad.gov.au/metadata/records/krill_risk_maps https://data.aad.gov.au/eds/3618/download http://doi.pangaea.de/10.1594/PANGAEA.826460 https://secure3.aad.gov.au/proms/public/projects/report_project_public.cfm?project_no=4037 http://data.aad.gov.au/aadc/metadata/citation.cfm?entry_id=krill_risk_maps |
| Summary: | 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). 2. Orr, J. C. et al. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437, 681-686 (2005). 3. Cao, L. et al. The role of ocean transport in the uptake of anthropogenic CO2. Biogeosciences 6, 375-390 (2009). 4. Yamanaka, Y. and Tajika, E. The role of the vertical fluxes of particulate organic matter and calcite in the oceanic carbon cycle: Studies using an ocean biogeochemical general circulation model. Glob. Biogeochem. Cycles 10, 361-382 (1996). 5. Key, R. M. et al. A global ocean carbon climatology: Results from Global Data Analysis Project (GLODAP). Glob. Biogeochem. Cycles 18, GB4031 (2004). 6. Conkright, M. E. et al. World Ocean Atlas 2001: Objective Analyses, Data Statistics, and Figures CD-ROM Documentation (National Oceanographic Data Center, 2002). Materials and Methods The experimental population of krill was collected from the Indian Ocean sector of the Southern Ocean at 64 degrees 09'S, 100 degrees 46'E during the 2010/11 field season. The krill were maintained in the Australian Antarctic Division's marine research aquarium, where they matured and spawned naturally. Experimental set-up Experimental sea water was supplied from a 70 l header tank and equilibrated with air (control) or CO2-enriched air before being delivered to experimental jars (250 ml clear polycarbonate) containing krill eggs. CO2-enriched air was prepared with a mass flow controller (Horiba STEC SEC-E-40) and by an air valve, to regulate flow rates of pure CO2 and atmospheric air, respectively. The pCO2 levels of the CO2-enriched air and sea water were monitored by a CO2 monitor (Telaire 7001) and indirectly from pH measurement (Mettler Toledo Seven Go Duo pro), respectively. Experimental temperature was set at 0.5 degrees C. Experiments were conducted in a refrigerator system maintained at 0.5 degrees C, equipped with 6 shelves. Each shelf was randomly assigned to a CO2 level treatment, and experimental jars were randomly distributed within each shelf. Effluent from each jar was drained into a 70 l sump, and recirculated through a degassing unit before returning back to the header tank via a filtration and cooling system. Total alkalinity was measured using an alkalinity titrator (Model Kimoto ATT-5). Hatching experiment Fertilized eggs were obtained in January 2012 from females that spawned in the laboratory. A total of eleven egg batches, each originating from different females, were used. Each batch was randomly distributed into experimental jars, with approximately 20-30 eggs per jar. Two types of experiments were conducted. Detailed response of hatch rates against increasing CO2 (experiment 1) For eight batches of eggs, the embryos were incubated at: 380 micro atm (control), 1000, 1250, 1500, 1750, and 2000 micro-atm pCO2. Approximately 20-30 eggs from each batch were randomly assigned to each of the pCO2 treatment levels. Critical timing of high CO2 exposure (experiment 2) Three batches of eggs were exposed to four different treatments, CC, 380 micro-atm CO2 throughout a total of 8 days (control), CH, 380 micro-atm CO2 for the first 3 days followed by 1750 micro-atm CO2 exposure for additional 5 days, HC, 1750 micro-atm CO2 for the first 3 days followed by 380 micro-atm CO2, and HH, 1750 micro-atm CO2 throughout. Hatch rates were determined for each jar 8 days after spawning. There were 6 replicates from each batch for each treatment. Modelling of hatch rates Experimental hatch rates were modelled using binomial mixed models with treatment (pCO2 level) as a factor and a random effect of egg batch. The models were fitted using WinBUGS 1.4.3, with a burn-in of 1000 iterations and parameter estimates obtained after a further 20000 iterations with a thinning rate of 10. Diffuse normal priors with a mean of zero and precision of 10^-6 were used for the treatment effects, and a diffuse gamma prior with shape and rate values of 0.001 for the random effect of batch. Variable names batch is the egg batch number for each jar eggs is the number of eggs in each jar hatched is the number hatched ppm is the pCO2 factor level [for experiment 1 only] (1=380 uatm pCO2, 2=1000 uatm pCO2, 3=1250 uatm pCO2, 4=1500 uatm pCO2, 5=1750 uatm pCO2, 6=2000 uatm pCO2) treatment is the treatment applied to the jar [experiment 2 only] |
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