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B C PlankTOM11 Structure I H PlankTOM11 O E The of in PlankTOM11 (concentration Z ) over time: An biogeochemical model j L M Biology as 11 Functional Types (PFTs) O I �� Gelatinous zooplankton have traditionally been ignored in studies and viewed as nuisance . Chemistry as 39 biogeochemical tracers, cycles of G C = � × � × ��� × � − � × � × � − � × � × � ° C, O2, P, Si, Fe, N, DOC and POC I A �� The term gelatinous zooplankton includes (true jellyfish), Ctenophora ( gooseberries) and Tunicata (salps). C L For my PhD I incorporated jellyfish (Cnidaria) into a global biogeochemical model. Jellyfish were selected from the three growth through grazing loss through grazing basal respiration gelatinous zooplankton types as they have the most available data suitable for biogeochemical modelling and compose the P NEMO v2.3 A H Lucas et al., 2014 A general circulation model L � largest biomass of the three types . Model Forced Y − � × � × × � Horizontal resolution 2° longitude, 1.1° latitude ° S � + � Vertical resolution of 10m (in top 100m) decreasing to I Daily stress, cloud cover mortality Jellyfish have the characteristics of 500m at depth and precipitation C (1) highly efficient grazers of zooplankton Atlas) A Explicit vertical mixing , P, N, Si, O2, DIC and (2) bloom forming organisms. Coupled to LIM (a thermodynamic sea-ice model) alkalinity “arguably the most important predators L (from the World Ocean Pauly et al., 2009 (from NCEP/NCAR) Jellyfish PFT metabolic rates of the sea, the jellyfish” (from GLODAP) These two characteristics are thought to have a role in the biological carbon pump The jellyfish PFT is parameterised using best A single jelly-fall provided four times (1) by acting as a control on the biomass of other zooplankton available data on metabolic rates, including the annual carbon input to the and therefore affecting the structure of the plankton ecosystem PlankTOM11 temperature-dependent growth through seafloor Lebrato and Jones, 2009 (2) by directly contributing to carbon export through bloom grazing, mortality and respiration. Shown PFT’s within PlankTOM interact via a food web, a simplified schematic, die-off and subsequent carcass sinking events known as jelly-falls below is the data gathered from the literature with energy transfer between types A single jelly-fall exceeded the annual (grey circles) and metabolic rates calculated downward flux of carbon by more than represented by arrows. The numbers However, these roles are poorly quantified through observations and using lines of best fit (three-parameter for an order of magnitude Billett et al., 2006 are the preference ratio of jellyfish have not yet been examined with the use of global ocean for its prey. Food web links of 0.1 Growth and two-parameter Q10 for Respiration

biogeochemistry models. I have added jellyfish to PlankTOM, an are not shown. picophyto N2 fixers coccolithophores mixed phyto phaeocystis and Mortality). ocean biogeochemistry model. 1 1 1 Growth rate (d-1) 0.4

0.2

The Biological proto- Carbon Pump zooplankton 0 0 10 20 30 The production of 7.5 meso- organic carbon in ocean zooplankton crustacean macro- Respiration rate (d-1) surface by 5 10 zooplankton 0.4 marine organisms and jellyfish its subsequent zooplankton transport to the seafloor 0.2

0 0 10 20 30

Mortality rate (d-1) Jellyfish mortality is the parameter with the highest 0.4 Turner, 2014 uncertainty due to limited experimental data. Extensive sensitivity and tuning mortality experiments were carried out due to this uncertainty. Jellyfish 0.2 mortality was found to exert significant control over

the global ecosystem. 0 0 10 20 30 a) Mean Global Jellyfish Biomass Temperature ºC

PlankTOM11: 0.13 PgC Jellyfish mortality rate (d-1) Observations: 0.1 – 0.46 PgC* In the small amount of data 0.12 0.02 available and suitable for use in surfacechlorophyll Annual mean ( PlankTOM11 gives a global jellyfish biomass the model (16 data points from μmol within the range of observations two studies) mortality ranged chl L - 1 from 0.006 – 0.026 per day. )

0.8 Applying the exponential fit to PFT carbon biomasses Annual mean surface Mean surface jellyfish biomass biomass jellyfish surface Mean this data gave a mortality rate 1985 L C (µmol 0.6

- at 0°C (� ) of 0.02 per day. Jellyfish 2015 (µmol C L ° - Sensitivity tests were carried 1) 0.4 out from this mortality rate due to low confidence in the value. - 1

0.2 )

Crustacean 0 Results from a subset of the sensitivity tests are shown. The model was found to best represent a range of observations when jellyfish mortality was increased to 0.12 per day. 1.2 Mortality rate values closer to 0.02 per day allowed Meso Surface jellyfish biomass 1985 Max jellyfish to dominate macro- and mesozooplankton, - 0.8 2015 (µmol C L greatly reducing their biomass. Low jellyfish mortality also resulted in higher chlorophyll concentrations

Mean than observed, especially in the high latitudes. The 0.4 -

1 Proto ) higher mortality rate may be accounting for the Min greater vulnerability to mortality experienced during 0.0 the early stages of the cycle. Latitude

b) Chlorophyll Jun - Aug Nov - Jan d) Export Efficiency

0.8 ( concentration Temperature and primary production have been found to be the key components in shaping the ef ratio, but simplistic surface chlorophyll The maps show the surface chlorophyll for Laws et al., 2000 observations (SeaWifs) and PlankTOM11. There is relationships such as a linear, negative correlation between ef ratio and temperature do not explain much of the SeaWiFS 0.6 global variance found in the ef ratio. The ef ratio varies proportionally to primary production, but both positive and negative excellent replication of chlorophyll in the Pacific observations 0.4 correlation have been found in different ocean regions, indicating that significant control over the export of carbon is µgL Ocean, and between 60°S - 30°S by the model. There external to primary production Henson et al., 2015, Cavan et al., 2017. -

0.2 1 is also good replication of chlorophyll patterns in the ) , but at lower levels. The lower coastal 0 chlorophyll generally (except around South America!) The carbon export is affected by several factors, including the primary production by , the types of Cavan et al., 2017 is usual in global models, due to the complexity of phytoplankton, the presence of bloom conditions, and the grazing by zooplankton . Thus, the composition and coastal regions, at resolutions higher than the model activity of the has a critical influence on the amount of carbon that is exported at depth. The composition set up. Overall PlankTOM11 does a good job of of zooplankton is increasingly recognised as a factor in determining carbon export and the ef ratio. However, the role of N Henson et al., 2015, Cavan et al., 2017 replicating global observed chlorophyll and zooplankton diversity has been little explored . PlankTOM11 T seasonality. model results Correlation over time of ef ratio with… S Primary Production Sea Surface Temperature The black boxes indicate the north (N) tropical (T) and 1 south (S) regions used in the bar charts Le Quéré et al., 2016. The inclusion of the jellyfish PFT in PlankTOM 0 improves the north/south (N/S) chlorophyll ratio over N that of TOM10 (no jellyfish) or TOM10.5 (jellyfish as 2 N -1 0.4 N crustacean). From the ratio results alone, TOM10.5 is N an improvement on TOM10, suggesting that adding 1.5 S S S an additional zooplankton PFT improves chlorophyll in Chlorophyll Total Zooplankton 0.2 S T the model. However, the regional chlorophyll 1

concentration T T T concentration shows that TOM10.5 underestimates N/S chlorophyll ratio N/S chlorophyll Regional chlorophyll the regional chlorophyll the most of the three models. 0.5 0 SeaWiFS TOM11 TOM10 TOM10.5 SeaWiFS TOM11 TOM10 TOM10.5

Identical to PlankTOM11 except for Identical to PlankTOM11 except for the Jellyfish the Jellyfish PFT is switched off, so PFT is turned into crustacean PFT, so that there Jellyfish Crustacean Zooplankton that there are 10 PFTs. are 11 PFTs with two crustacean PFTs.

c) Carbon Export in the The export efficiency ratio (ef ratio) is quantified as the ratio of the flux of organic carbon exported across the base of the euphotic zone (here defined as 100m) to the integrated primary production within that layer Laws et a., 2000 ef ratio = Export Production Primary Production Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0.25 1.2 0.4 0.2 ef ratio PlankTOM11 replicates the complexity of processes influencing carbon export as 1

Primary Production neither primary production, sea surface temperature or zooplankton, predicted (mol C/m (mol

) 1 1

- ef ratio. 20 0.8 0.1 ef ratio 0.2 Primary / Export Production (mol C/m /year) Export Efficiency Export 2 m 2

10 /year)

µmol C L µmol C The greater seasonality in ef ratio at high latitudes is driven by greater 0.6 0.8 Jellyfish Primary Production

mol C/ mol seasonality in primary production and a greater lag to secondary production 0 0.15 (zooplankton). This lag between primary and secondary production explains the 0.4 Crustacea

ExportProduction 0.6 negative correlation between ef ratio and primary production shown in (mol C/m (mol 100m Average ( 100m Average 4 - n 0.2 PlankTOM11. 0.1 2 Export Production ( Production Export 2 /year) 0.4 0 In PlankTOM11 jellyfish have a stronger positive correlation to the ef ratio than 2

0 2 4 6 8 /year) 0 crustacean zooplankton or total zooplankton, indicating that jellyfish may play a Primary Production (mol C/m2/year) 0.05 ExportEfficiency Ratio 0.2 key role in controlling the mechanisms of carbon export.

Zooplankton Biomass 0 Export Production 0.6 In the South (30:90ºS) the correlation between primary production and export production is positive and follows 0 0 0.3 a seasonal pattern, with higher production in the Jan Mar May Jul Sep Nov 0 summer-autumn, lower production in winter-spring, and a higher ef ratio in the winter.

References Billett et al., 2006. Mass deposition of jellyfish in the deep Arabian Sea. and Oceanography. 51(5): 2077-2083. Lebrato and Jones 2009. Mass deposition event of Pyrosoma atlanticum carcasses off Ivory (West Africa). Limnology and Oceanography. 54(4): 1197-209. *From a range of studies & my own analysis of the MAREDAT database: Cavan et al., 2017. Role of zooplankton in determining the efficiency of the biological carbon pump. Biogeosciences. 14: 177-186. Lucas et al., 2014. Gelatinous zooplankton biomass in the global ; geographic variation and environmental drivers. Global Ecol. Biogeogr. 23(7): 701-714. Lucas et al., 2014 Henson et al., 2015. Variability in efficiency of particulate organic carbon export: A model study. Global Biogeochemical Cycles. 29: 33-45. Pauly et al., 2009. Jellyfish in , online databases, and ecosystem models. Hydrobiologia. 616: 67-85. Bar-On et al., 2018. The biomass distribution on . Proceedings of the National Academy of Sciences. 115(25): 6506-11. Laws et al., 2000. Temperature effects on export production in the open ocean. Global Biogeochemical Cycles. 14: 1231-1246. Turner, 2014. Zooplankton fecal pellets, , phytodetritus and the ocean’s . Progress in Oceanography. 130: 205-48. Buitenhuis et al., 2013. MAREDAT: towards a world atlas of MARine Ecosystem DATa. Earth System Science Data. 12(5): 227-39.