The Marine chapter of the 2016 State of the Environment (SoE) report incorporates multiple expert templates developed from
streams of marine data. This metadata record describes the Expert Assessment "The state and trends of habitats and communities
- water column, outer shelf (25 m - 250 m)". The full Expert Assessment, including figures and tables (where provided),
is attached to this record. Where available, the Data Stream(s) used to generate this Expert Assessment are accessible through
the "On-line Resources" section of this record.
DESCRIPTION OF HABITAT/COMMUNITY FOR EXPERT ASSESSMENT
The water column is the primary habitat for pelagic communities that are mainly phytoplankton, bacteria, zooplankton and
higher predators (in the ratios of ~300:75:10:1, respectively: Marchant 2002) and their biomass declines exponentially with
depth (Rex et al., 2006). The major determinants of habitat quality for most pelagic organisms can be considered to be temperature
(T), salinity (S), light, nutrients, dissolved oxygen (DO), pH, and food availability. The continental shelf waters around
Australia are generally warm, saline, well illuminated, low in nutrients, and abundances of phytoplankton (Fig. 1), zooplankton
(Fig. 2) and fish. Relative to the seasonal variability for the majority of the water column on the outer shelf there have
been modest long term changes to these components of the habitat and its communities. Overall its current status should
be considered good. The major potential threats that could reduce the existing flora and fauna can be considered to be:
inputs from the terrestrial environment (sediments, nutrients, carbon), development, warming, declining [DO], decreasing pH
and fishing. While there are areas of local habitat degradation (e.g. near ports and harbours) the overall impacts of local
pressures tend to be low as Australia is a large area with a relatively sparse human population. There is increasing evidence
our shelf waters are experiencing change due to the global pressures; some of which are deleterious. Shelf waters from
Port Hedland to Cape Howe have risen ~ 1°C from 1993 to 2013 (Foster et al., 2014), and portions of the SW region were 3°C
warmer during February 2011 than normal (Pearce and Feng 2013). There is evidence that dissolved oxygen has declined (Thompson
et al. 2009) and continues to decline due to warming (Talley et al., 2016) plus concerns over acidification continue to
grow (Mongin et al., 2016). Already there is clear evidence of community responses by phytoplankton, zooplankton and fish
to these climatic pressures (e.g. Johnson et al. 2011).
DATA STREAM(S) USED IN EXPERT ASSESSMENT
Data are computed from the level 3 (L3) daily global products using one merging method following Maritorena and Siegel, (2005).
Details can be found at http://www.globcolour.info/products_description.html
Zooplankton data are from Australia’s National Reference Stations operated by the Integrated Marine Observing System.
2016 SOE ASSESSMENT SUMMARY [see attached Expert Assessment for full details]
• 2016 •
Assessment grade: Good
Assessment trend: Unclear
Confidence grade: Limited evidence or limited consensus
Confidence trend: Limited evidence or limited consensus
Comparability: Grade and trend are somewhat comparable to the 2011 assessment
• 2011 •
Assessment trend: Stable
CHANGES SINCE 2011 SOE ASSESSMENT
This assessment uses observations from a greater range of spatial and temporal scales allowing a much better assessment
of current state and trends to be made. There is also a statistical analysis of temporal change in zooplankton biomass.
Ajani, P.A., Allen, A.P., Ingleton, T., Armand, L., 2014. A decadal decline in relative abundance and a shift in microphytoplankton
composition at a long-term coastal station off southeast Australia. Limnol. Oceanogr., 59 (2014), pp. 519–531
Armbrecht, L.H., Schaeffer, A., Roughan, M., Armand, L.K. 2015. Interactions between seasonality and oceanic forcing drive
the phytoplankton variability in the tropical-temperate transition zone (~30°S) of Eastern Australia. J. Mar. Syst., 144:
Armbrecht, L.H., Thompson, P.A., Wright, S.W., Schaeffer, A., Roughan, M., Henderiks, J., Armand, L.K. 2015. Comparison
of the cross-shelf phytoplankton distribution of two oceanographically distinct regions off Australia. Journal of Marine Systems
Balzano, S., Ellis, A.V., Le Lana, C., Leterme, S.C. 2015. Seasonal changes in phytoplankton on the north-eastern shelf
of Kangaroo Island (South Australia) in 2012 and 2013. Oceanologia 57: 251–262 doi:10.1016/j.oceano.2015.04.003
Buchanan, P.J., Swadling, K.M., Eriksen, R.S., Wild-Allen, K. 2013. New evidence links changing shelf phytoplankton communities
to boundary currents in southeast Tasmania. Rev Fish Biol Fish. doi:10.1007/s11160-013-9312-z
Campbell, A., Hudson, D., Nicholls, C., Pointon, A. 2013. Tactical Research Fund: Review of the 2012 paralytic shellfish
toxin event in Tasmania associated with the dinoflagellate alga, Alexandrium tamarense, FRDC Project 2012/060, SafeFish,
Adelaide. ISBN: 978-0-646-90570-9.
Condie, S.A., Dunn, J.R. 2006. Seasonal characteristics of the surface mixed layer in the Australasian region: implications
for primary production regimes and biogeography. Mar. Freshw. Res. 57: 569.
Everett, J.D., Baird,M.E., Roughan, M., Suthers, I.M., Doblin, M.A., 2014. Relative impact of seasonal and oceanographic
drivers on surface chlorophyll a along a Western Boundary Current. Prog. Oceanogr., 120: 340–351.
Feng, M., McPhaden, M. J., Xie, S., & Hafner, J. 2013. La Niña forces unprecedented Leeuwin Current warming in 2011. Scientific
Reports 3, 1277; DOI:10.1038/srep01277.
Foster, S.D., Griffin, D.A., Dunstan, P.K. 2014. Twenty Years of High-Resolution Sea Surface Temperature Imagery around Australia:
Inter-Annual and Annual Variability. PLoS ONE 9(7): e100762. doi:10.1371/journal.pone.0100762
Grant, B.R. 1971. Variation in silicate concentration at Port Hacking station, Sydney, in relation to phytoplankton growth.
Aust. J. Mar. Freshw. Res. 22: 49-54.
Johnson CR, Banks SC, Barrett NS; Cazassus F; Dunstan PK; Edgar GJ; Frusher SD; Gardner C; Helidoniotis F; Holbrook NJ; Ling
SD; Melbourne-Thomas J; Miller K; Pecl GT; Ritz DA; Ross DJ; Sanderson JC; Swadling KM. 2011. 'Climate change cascades:
Shifts in oceanography, species' ranges and subtidal marine community dynamics in eastern Tasmania', Journal of Experimental
Marine Biology and Ecology, 400, (1-2) pp. 17-32. ISSN 0022-0981 (2011) [Refereed Article]
Kelly, P., Clementson, L., Lyne, V. 2015. Decadal and seasonal changes in temperature, salinity, nitrate, and chlorophyll
in inshore and offshore waters along southeast Australia. J. Geophys. Res. Oceans, 120: 4226.
Maritorena, S. and Siegel, D.A. 2005. Consistent Merging of Satellite Ocean Colour Data Sets Using a Bio-Optical Model. Remote
Sensing of Environment, 94, 4, 429-440.
Matear, R. J., M. A. Chamberlain, C. Sun, and M. Feng. 2013. Climate change projection of the Tasman Sea from an Eddy-resolving
Ocean Model, J. Geophys. Res. Oceans, 118: 2961–2976
McLeod, D.J., Hallegraeff, G.M., Hosie, G.W., Richardson, A.J. 2012. Climate-driven range expansion of the red-tide dinoflagellate
Noctiluca scintillans into the Southern Ocean. J. Plankton Res., 34: 332–337
Mongin, M., Baird, M.E., Tilbrook, B., Matear, R/J., Lenton, A., Herzfeld, M., Wild-Allen, K., Skerratt, J., Margvelashvili,
N., Robson, B.J., Duarte, C.M., Gustafsson, M.S.M., Ralph, P.J., Steven, A.D.L. 2016. The exposure of the Great Barrier
Reef to ocean acidification. Nature Communications DOI: 10.1038/ncomms10732
Oliver, E. C. J., and N. J. Holbrook. 2014. Extending our understanding of South Pacific gyre “spin-up”: Modeling the East
Australian Current in a future climate, J. Geophys. Res. Oceans, 119, 2788–2805, doi:10.1002/2013JC009591.
Watson, R.A., G.B. Nowara, S.R. Tracey, E.A. Fulton, C.M. Bulman, G.J. Edgar, N.S. Barrett, J.M. Lyle, S.D. Frusher, C.D.
Buxton. 2013. Ecosystem model of Tasmanian waters explores impacts of climate-change induced changes in primary productivity.
Ecol. Model., 264 : 115–129.
Siegel DA, Behrenfeld MJ, Maritorena S, McClain, C.R., Anoine, D., Bailey, S.W., Botempi, P.S., Boss, E.S., Dierssen, H.M.,
Doney, S.C., Eplee, Jr., R.E., Evens, R.H., Feldman, G.C., Fields, E., Franz, B.A., Kuring, N.A., Mengelt, C., Nelson,
N.B., Patt, F.S., Robinson, W.D., Sarmento, J.L., Swan, C.M., Werdell, P.J., Westbury, T.K., Wilding, J.G., Yoder, J.A. 2013.
Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission. Remote Sensing of Environment, 135, 77–91.
Thompson, PA, Bonham, P, Rochester, W, Doblin, MA, Waite, AM, Richardson A, Rousseaux C. 2015. Climate variability drives
plankton community composition changes: an El Niño to La Niña transition around Australia. J. Plankton Res. 37(5): 966–984.
Thompson, P.A., Baird, M.E., Ingleton, T., Doblin, M.A. 2009 Long-term changes in temperate Australian coastal waters and
implications for phytoplankton. Marine Ecology Progress Series 384: 1-19.
Thompson, P.A., T. O’Brien, K. Isensee, L. Lorenzoni, L. E. Beckley, M.W. Lomas, A. Bode, F. Muller-Karger, L. Valdés, P.
Wiebe et al. 2016. The South Pacific Ocean. In, O’Brien, K. Isensee, L. Lorenzoni, L., [Eds.]. UNESCO. In review.
QUALITY OF DATA USED IN THE ASSESSMENT
Quality of data used in the assessment
Spatial and temporal coverage are excellent. Conversion of ocean colour to chlorophyll a introduces a source of potential
error that decreases with depth. Data could be extracted for each region to allow improved regional assessments of state
CUSTODIAN AND LOCATION OF DATA
The remotely sensed data and details can be found at http://www.globcolour.info/products_description.html
The in situ zooplankton data can be found at: https://portal.aodn.org.au/search (see links in On-line resources section of this record for specific datasets)
METHOD USED TO DETERMINE STATE OR RECENT TREND
Linear regression fit to each pixel over the duration from 1997 to 2015.
When citing this Expert Assessment in a list of references use the following format:
citation author name/s (year metadata published), metadata title. Citation author organisation/s. File identifier and Data
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