Predicting phytoplankton blooms

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1 Predicting phytoplankton blooms Peter Thompson CSIRO OCEANS & ATMOSPHERE

nutrients Trophic transfer, nutritional value, selective grazing Field work on phytoplankton (~ 24

2 How to make phytoplankton ecology more quantitative and predictive? Personal experience Laboratory studies (~11 years, 4 papers) Responses to light, temperature and nutrients Trophic transfer, nutritional value, selective grazing Field work on phytoplankton (~ 24 years, 7 papers) Case studies Modelling Analysis Power of time series data to predict the future 2 Phytoplankton and climate Peter Thompson

very experienced HABS researchers Data managed by Todd O Brien

3 THREE International Groups SCOR WG 137, IOC UNESCO TrendsPO, IOC UNESCO IGMETS Diverse groups from around the planet with some very experienced HABS researchers Data managed by Todd O Brien (NOAA) Basic data analysis is available online using tools Todd has built.

Cloern, J. E. and Jassby, A. D.: Patterns and Scales of Phytoplankton Variability in Estuarine-Coastal Ecosystems, Estuar. Coast., 33, 23 241, doi: 1.

4 Cloern, J. E. and Jassby, A. D.: Patterns and Scales of Phytoplankton Variability in Estuarine-Coastal Ecosystems, Estuar. Coast., 33, , doi: 1.17/S , 21. Motivation: Can we make phytoplankton ecology more predictive? Cloern and Jassby asked Why does phytoplankton biomass fluctuate mildly in some places and wildly in others?

5 Swan Estuary Eutrophic Productive Blooms Karlodinium veneficum Microcystis aeruginosa The Swan Estuary has a catchment area of ~ 121, km 2 PETER THOMPSON CSIRO.

6 Swan River sampling stations Phytoplankton sampling (+ other water quality parameters) is ~ weekly since 1994 at 9+ stations. Indian Ocean

7 Climate PETER THOMPSON CSIRO.

8 rainfall (mm/month) rainfall (mm/month) rainfall (mm/month) rainfall (mm/month) Climate 6 December June March Sept CSIRO January July April Oct February August Nov May Drying, yes.but this often has a seasonal component Strongest declines are: May (-.8mm/y, p=.2) June (-1.6mm/y, p=.3) October (-.38mm/y, p=.2). Less in May, June and October = a longer dry season

9 Swan River example for dinoflagellates Rainfall (mm/month) (A) May rainfall (C) Swan Estuary Blackwall Reach Dinoflagellates L -1 Lower precipitation in May and June Less river flow River flow (m 3 /s) (B) June river flow (D) Swan Estuary Armstrong Spit Dinoflagellates L -1 Fewer dinoflagellates at the most oceanic sites in May and June Time (year) Rainfall (mm) 9 HAB and climate Peter Thompson Hydrology and Phytoplankton

10 Seasonal Patterns for dinoflagellates in the Swan Estuary Autumn Winter Spring Jan Feb Mar April May June July Aug Sept Oct Nov Dec Swan River Estuary - S1 Blackwall Reach (Australia) p< p< p< p< p< p< p<.1 Swan River Estuary - S2 Armstrong Spit (Australia) p< p< p< p<.1 Swan River Estuary - S3 Narrows Bridge (Australia) p< p< Swan River Estuary - S4 Ron Courtney Island (Australia) p< p< p< p< p< p<.1 Swan River Estuary - S5 Success Hill (Australia) p< p< p<.5 Dinoflagellates were positively correlated with autumn and early winter precipitation at lower estuary sites. A drying climate is reducing these blooms at some sites. Does drying reduce autumn dinoflagellate blooms generally? 1 HAB and climate Hydrology and phytoplankton

11 IPCC 214 Jun Jul - Aug

12 Sites: up to 132 depending on question Continent area Water body (number of sites within) Asia SE Hong Kong waters, (n = 1) Australia SW Swan River estuary, (n = 5) Longitude North America 2 SW San Francisco Bay, (n = 7) SE Dec Nov Oct Sept Aug July June May Apr Mar Feb Precipitation (mm month ) wet & wetter dry & dryer other Jan Latitude 6 Time (month of year) Other = regions where lag between precipitation and flow was expected to be very long in winter Precipitation data from NOAA s Earth System Research Laboratory quality controlled precipitation data set based on 67,2 rainfall stations worldwide (Schneider et al., 211). (Data corrected for hemisphere) South America Europe NE NE NE SE Neuse River and Pamlico Sound, (n = 2) Narragansett Bay, (n = 1) Booth Bay, Maine, (n = 1) Bay of Fundy, n = 5) Patos Lagoon Estuary, (n = 1) N Skagerrak, Kattegat, (n=3) NW North Sea, English Channel, Irish Sea, (n = 8) N Baltic Sea, (n = 37) SW Guadiana Estuary, (n = 1) SW Nervion River Estuary, (n = 2) SW Bay of Biscay, (n = 5) SW Mediterranean, (n = 5)

13 Proportion of positive time series (%) Timing of precipitation Generally there were positive responses during summer (P =.18) P=.7 P=.9 chla diatoms dinos chloro chryso eugleno r 2 =.38 P=.2 P=.15 P=.15 All sites P=.4 winter spring summer autumn P=.6 Season P=.3 P=.6 Winter Winter precipitation was negatively associated with chlorophyll a, diatoms and chrysophytes. For diatoms negative associations with precipitation were dominant in January & February. Spring Diatoms were negatively associated with precipitation in March and April while chlorophyte abundances increased with precipitation. Summer Chlorophyll a and Chlorophytes were positively associated with precipitation. Autumn Dinoflagellates were negatively associated with increased precipitation, similarly diatoms during October. 13 HAB and climate Peter Thompson Hydrology and Phytoplankton

14 Long term average abundance (cells L -1 ) 1 8 North Sea (Baltic) (A) Chlorophytes Arhus Bugt (Baltic) Koge Bugt (Baltic) 1 7 Hevring Bugt (Baltic) Ringkobing Fjord (Baltic) Nissum Fjord (Baltic) Nissum Bredning (Baltic) 1 6 Logstor Bredning (Baltic) Skive Fjord (Baltic) Lister Dyb (Baltic) 1 5 Alborg Bugt (Baltic) Anholt East (Baltic) Vejle Fjord (Baltic) Ven (Baltic) 1 4 Arkona (Baltic) Mariager Fjord (Baltic) Horsens Fjord (Baltic) Roskilde Fjord (Baltic) 1 3 Lillebaelt-South (Baltic) Lillebaelt-North (Baltic) Odense (Baltic) Gniben (Baltic) (B) Dinoflagellates salinity Storebaelt (Baltic) 1 7 Bornholm Deep (Baltic) Swan River-Blackwall (Australia) Swan River-Armstrong (Australia) 2D Graph Swan RIver-Narrows (Australia) Swan RIver-Courtney (Australia) Swan River-Success (Australia) 1 5 San Francisco-lower south (USA) 3.5 San Francisco-mid south (USA) 3. San Francisco-north south (USA) San Francisco-central bay (USA) 2. San Francisco-San Pablo (USA) 1.5 San Francisco-Suisun (USA) San Francisco-Sacramento (USA) Patos Lagoon (Brazil).5 REPHY Antifer (English Channel) REPHY At So (English Channel) REPHY Donville (English Channel) REPHY Pen (English Channel) REPHY Point SNR (English Channel) Bay of Fund-Brandy Cove (Canada) (C) Chrysophytes Bay of Fundy-Deadmans Harbour (Canada) Bay of Fundy-Lime Kiln Bay (Canada) Bay of Fundy-Passamaquoddy (Canada) 1 6 Bay of Fundy-Wolves Is. (Canada) SMHI A17 (Sweden) SMHI Anholt East (Kattegat) SMHI Slaggo (Sweden) 1 5 AZTI D2 (SE Bay of Biscay) Nervion River E1 (southern Bay of Biscay) Nevion River E2 (southern Bay of Biscay) REPHY Kervel (Bay of Biscay) REPHY Le Cornard (Bay of Biscay) 1 4 REPHY Men de Roue (Bay of Biscay) REPHY Quest Loscolo (Bay of Biscay) REPHY Teychan Bis (Bay of Biscay) REPHY Diana Centre (Mediterranean) 1 3 REPHY Lazaret (Mediterranean) REPHY Parc Leucate (Mediterranean) REPHY Villefranche (Mediterranean) 14 HAB and 1 climate 2 3 Peter Thau Thompson Lagoon (Mediterranean) Hydrology and Phytoplankton Y Data Salinity Using long term averaged values. Longest time series was 33 years of monthly sampling, n~ 396. A pattern of response for chlorophytes (advection possible but unlikely to be the primary driver)

15 Conclusions from this study Phytoplankton community composition showed changes associated with climate variability. Changes in seasonal precipitation were quantitatively linked with responses by particular phyla. Decreasing precipitation was associated with fewer chlorophytes. Increasing precipitation affected spring diatom blooms and truncated autumn dinoflagellate blooms. The timing and magnitude of precipitation influenced phytoplankton community composition in a predictable manner. 15 HAB and climate Peter Thompson Hydrology and Phytoplankton

16 Way forward? Dynamic mechanistic model Improved statistical model ~ 8 HAB species in Australia Harvest existing date Determine what conditions favour blooms by each species in Australia and worldwide Temperature, salinity, nutrients, time of year etc., Use these relationships to make predictions about blooms Some closing observations

17 Diatom example 25 sites in 4 regions 12 monthly time series ~ 3 correlations Probability of getting 4% positive slopes from 292 is very small. (assumes a normal distribution and random observations) Z Test for the Proportion number of correlations 292 number of positive slopes 117 Sample Proportion.4685 Null Hypothesis p=.5 Standard Error.2926 a.5 Z Test Statistic Two-Tailed Test Lower Critical Value Upper Critical value p-value.688 Decision Reject

Drying regions proportion of positive time series (%) 9 8 7 6 5 4 3 (A)

1 P=.3 chla diatoms dinos chlorophytes Only chlorophytes showed a consistent

18 Drying regions proportion of positive time series (%) (A) Regions of decreasing precipitation winter spring summer autumn P <.8 P=.1 P=.3 chla diatoms dinos chlorophytes Only chlorophytes showed a consistent response to more precipitation Over whole year (P=.8) Also during spring and summer

Wet and getting wetter proportion of positive time series (%) 7 6 5 4 3 2 (A) Regions of increasing precipitation P=.55 P=.7 P=.18 P=.28 P=.

19 Wet and getting wetter proportion of positive time series (%) (A) Regions of increasing precipitation P=.55 P=.7 P=.18 P=.28 P=.21 winter spring summer autumn chla diatoms dinos chlorophytes Chlorophyll a responds positively to precipitation in autumn Diatoms negative overall all seasons esp. spring Dinos were mixed up in summer down in autumn

20 SW Australia: Long term drying trend Rainfall down ~ 1 mm relative to long term mean Rainfall has considerable inter annual variability but shows a significant long term decline. PETER THOMPSON CSIRO.

Changing Hydrology under a Changing Climate for a Coastal Plain Watershed

Changing Hydrology under a Changing Climate for a Coastal Plain Watershed David Bosch USDA-ARS, Tifton, GA Jeff Arnold ARS Temple, TX and Peter Allen Baylor University, TX SEWRU Objectives 1. Project changes