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Long-term observations in coastal areas: a key tool for understanding the functioning of a system and detecting changes Maurizio Ribera d’Alcalà Stazione Zologica Anton Dohrn, Napoli, Italy The Universe of Ptolemaeus Message 1 Careful observations over time allows for detecting patterns on which it is possible to build semiempirical reconstruction of the time course of a process The Universe of Kepler Message 2 and 3 1. A reanalysis of the ‘same’ observations may stimulate an improved reconstruction of the time course of a process 2. In turn, the reconstruction may become the basis for detecting a mechanism beyond the pattern Hidden and visible Sapropels Cramp & Sullivan, 1999 Message 4 It is not necessary to observe a process while it is occurring, as long as it has left traces in time. Still, we have to gather observations on the traces. Isotopic signal in deep sea cores Lourens, 2004 Laskar, 1993 Message 5 Also records may play the role of suggesting mechanisms. Though, they are not always testable The basis of modern biological oceanography Sverdrup, 1959 Message 6 A hypothesis on a mechanism behind a process, again stimulated by observations, may be tested with a time series of other observations Napoli time series Marechiara ° 40.90 Naples N 200 MC 100 100 Ischia 200 ° 40.70 500 Ty rrh en ian ° 40.50 ° 13.80 Ad ria tic Se a Se a 200 500 Capri ° 14.00 ° 14.20 Sampled fortnightly in 1984-1991 and weekly from 1995 to present 100 200 500 ° 14.40 Monthly averages Salinity 0-10 m Temperature 0-10 m Mixed Layer Depth (m) Monthly averages µmol L-1 TIN 0-10 m SiO4 0-10 m PO4 0-10 m The seasonal cycle of chl.a Chla - 0 m mg m-3 Int Chla 0-70 m mg m-2 Phytoplankton - 0m Abundance Biomass μg C l-1 Cells ml - 50000 1 5000 500 100 Percentage Phytoplankton Biomass - 0m 100 80 60 40 20 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Diatoms Coccolithophores Dinoflagellates Other flagellates Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DIATOMS DINOFLAGELLATES COCCOLITHOPHORES OTHER FLAGELLATES Message 7 Phytoplankton may be differently distributed in the water column in different seasons, therefore surface chl.a concentration may mislead the interpretation of the seasonal cycle Species phenology 2006 2004 2002 2000 1998 1996 1994 1992 1990 1988 1986 1984 J F M A M J J A S O N DJ J F M A M J J A S O N JD F M F M A M J J A S O N DJ F M A M J J A S O N D 2006 2004 2002 2000 1998 1996 1994 1992 1990 1988 1986 1984 A M J J A S O N D J F M A M J J A S O N D Message 8 Phytoplankton species display a phenology Trophic conditions TIN START OF THE BLOOMS 16 10 14 12 8 mol/dm3 DECLINE OF THE BLOOMS 10 6 8 4 6 4 2 2 0 0 Max Min 75th % 25th % Median Zingone, Sarno, Nardella & Licandro, in preparation Temperature START OF THE BLOOMS DECLINE OF THE BLOOMS 30 28 28 26 26 24 24 °C 22 20 22 20 18 18 16 16 14 14 12 12 Max Min 75th % 25th % Median Zingone, Sarno, Nardella & Licandro, in preparation Synchronous patterns in the occurrence of the species FV PO FS cells ∙ ml-1 NA FP PT PL 30 1800 1800 20 1200 1200 10 600 600 0 0 0 400 800 800 200 100 400 0 0 0 400 4000 4000 200 2000 2000 0 200 0 4000 0 4000 100 2000 2000 0 1000 0 0 100 100 500 50 50 0 0 0 40 2 2 20 1 1 0 40 0 0 2 2 20 1 0 MA 2002 MA ‘02 MA 2003 MA ‘03 MA 2004 MA ‘04 MA 2005 MA ‘05 NA FS FP P PL cells ∙ ml-1 PO 1 0 J 2001 J ‘01 AM J 2002 AM J 2003 AM J 2004 AM 2005 0 ‘01 4500 600 400 3000 400 200 1500 200 0 400 0 0 20000 1600 200 10000 800 0 400 0 20000 1000 200 10000 500 0 0 0 1000 10000 600 500 5000 300 0 400 0 2000 100 200 1000 50 0 0 0 60 200 4 30 100 2 0 0 0 60 50 4 30 25 JJ 2001 JJ ‘01 M JJ 2002 MJJ ‘02 M JJ 2002 MJJ ‘03 M JJ 2002 MJJ ‘04 M 2005 M ‘05 0 MJ J 2002 MJJ ‘02 MJ J 2003 MJJ ‘03 MJ J 2004 M 2005 ‘04 ‘05 MJJ M Cerataulina pelagica 600 0 JJ 2001 JJ AMJ AMJ AMJ AMJ ‘02 ‘03 ‘04 ‘05 Leptrocylindrus danicus Pseudoscurfieldia marina FV Chaetoceros throndsenii Prorocentrum triestinum Bacteriastrum furcatum 0 0 2 JA 2001 JA ‘01 JA 2002 JA 2003 JA 2004 ‘02 ‘03 ‘04 JA JA JA 0 JA 2001 JA ‘01 JA 2002 JA ‘02 JA 2003 JA ‘03 JA 2004 JA ‘04 Siano et al., in prep. Message 9 Species growth and accumulation in phytoplankton is not linearly linked with proximate abiotic factors. Indeed plankton display the cabability to extert control on their life cycle Looking for trends µg L-1 Chla 0m PSU Salinity 0m Looking for trends Message 10 Time series, even of basic hydrographic parameters highlight mid-term trends, if any cells ml-1 Looking for trends Phytoplankton abundance Looking for trends Diatom abundance Mean diatom size 100 90 19 17 15 13 11 50 9 40 7 30 20 5 10 3 0 1 ESD (µm) -1 60 cells 10 ml 70 3 80 Total carbon < 10 µm 10 - 15 µm > 15 µm 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1991 1990 1989 1988 1987 1986 1985 1984 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Message 11 Size may be an indicator of possible change in the structure of plankton community Factors/Issues Climate Change Atmospheric Deposition Basin Scale Oscillations Alien Species Land Based Inputs Hydrological Cycle (and flushing) Seasonality Coherence Episodic vs. Chronic Salinity and Precipitation Seasonal shift in wind direction Interannual variability in wind direction Nutrient load tonn y-1 Indirectly estimated loads 6000 5000 4000 3000 2000 1000 0 N load P load 1971 1981 1991 1996 Large scale dynamics Rio et al., 2006 Message 12 There is a wealth of easily accessible, easily produceable data that can help understanding what modulates the system functioning Salinity and Chlorophyll a shifts 2 sample Kolmogorov-Smirnov test Null Hyp.: Parameter 1984-1988 true distrib. funct. is not > true distrib. funct. 2003-2006 Chl.a (0 m) 1984-1988 is stochastically > Chl.a (0 m) 2003-2006 p=0.003 Chl.a (0-70 m) 1984-1988 is stochastically > Chl.a (0-70 m) 2003-2006 p=0.03 S (0-10 m) 1984-1988 is stochastically > S (0-10 m) 2003-2006 p=0.06 Phase variability (decade of the month) T min Start of Stratification Max time for Tsurf=III° End low S 1984 March II April II November II July 4 1985 March II April II November II July 4 1986 March II April II November II July 4 1987 March I April I November II July 4 1988 March I April I November I July II 1989 March I April I October I July I 1995 April II November I July I 1996 March I August II 1997 March I May I October II July I 1998 March II April II October II July I 1999 February II April III October III July I 2001 March I May I November III August I 2002 January I April I October III July I 2003 March II April II October II giugno I 2004 February I April II November II July III 2000 Idealized shapes Thalassiosira rotula Calciopappus caudatus Protoperidinium diabolus Phaeocystis sp. Shape relative abundance Interannual variability in composition Message 13 Very simple analyses with easy to determine indicators and public domain data may help in characterizing patterns and, possibly, in unveiling underlying mechanisms Phenology of Centropages typicus Mazzocchi et al., 2007 Large scale forcing Mazzocchi et al., 2007 The Zooscan • Copepod automatic Identifier (TP 93%) • Size as a community descriptor Number Copepod size spectrum Size (ESD) Digitalisation of >600 samples with the ZOOSCAN Total copepod abundance Diversity of size classes Shannon index (Parson, 1969, Ruiz, 1994) 1 Size class ~ 1 Species H ' p log p i 2 i Message 14 Not always new technologies are expensive and some may give significant information HABs dynamics A misleading view regarding HABs is that they are always irregular, unpredictable and conspicuous events involving the accumulation of highly concentrated populations. In fact, many of the highly toxic species often constitute a regularly occurring component of normal phytoplankton populations and can exert their impact at low cell concentrations (100-1000 cells·l-1) Zingone & Wyatt, 2006 HABs dynamics Harmful species are not equally dangerous throughout the year, rather they have generally one/several predictable/unpredictable periods of the year when they may exert their harmful effects Zingone & Wyatt, 2006 The history is recorded in the sediments Zingone & Wyatt, 2006 HAB forming species and HABs Zingone & Wyatt, 2006 Message 15 The presence of harmful species at given sites is a necessary but not sufficient condition for the development of harmful algal blooms, so that the geographic distributions of HABs do not necessarily strictly reflect those of the causative species Zingone & Wyatt, 2006 Responses to HABs Intense monitoring activities of causative organisms and/or toxins Development of alert systems based on automated observations coupled with predictive models that can expand the lead-time to harmful events, so as to allow more cost effective mitigation operations Progress in modelling is however seriously hampered by the lack of knowledge on the basic mechanisms underlying the development of specific algal blooms Zingone & Wyatt, 2006 An intriguing species 80 55 75 50 70 45 65 40 60 35 55 30 50 25 45 700 40 600 35 20 30 500 Percentage of cells Size classes (µm) P. multistriata cell size distribution 1996-2006 15 10 100 200 300 400 500 600 100 200 300 400 500 600 5 Cell/ml 400 300 200 100 0 0 Time ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ’02 ’03 ’04 ’05 ’06 D’Alelio et al., in prep. Possible scenarios a 100 b c 50 0 0 0 300 300 300 360 360 360 660 660 660 720 720 720 1020 1020 1020 1080 1080 1080 1380 1380 1380 1440 1440 1440 1740 1740 1740 100 50 100 50 100 50 100 50 % 100 50 100 50 100 50 100 50 100 50 100 50 0 1800 30 40 50 60 70 80 1800 30 40 50 60 70 80 1800 30 40 50 60 70 80 Cell size (µm) D’Alelio et al., in prep. Model for hindcasting 80 Cell size (µm) 70 60 50 40 30 28-Oct-95 11-Mar-97 24-Jul-98 06-Dec-99 19-Apr-01 01-Sep-02 14-Jan-04 28-May-05 10-Oct-06 Time D’Alelio et al., in prep. Message 16 Very simple models integrate observations and help in testing hypothesis on mechanisms A highly seasonal species Temora stylifera total population abundance at st. 'MC' (Gulf of Naples) 900 800 700 ind. m -3 600 500 400 300 200 100 0 1984 ’85 ’86 ’87 ’88 ’89 ’90 ’97 ’98 ‘99 Mazzocchi et al., 2006 A highly seasonal species Mean seasonal cycle of T. stylifera at st. ‘MC’ 400 40 350 juv. ind. m -3 300 30 250 200 20 150 100 10 50 0 0 J F M A M J J A S O N D Mazzocchi et al., 2006 Another hindcasting model THE MODEL STRUCTURE stages 1 eggs 2 NI 3 NII-NVI 4 CI 5 CII-CV 6 adults (f, m) Mazzocchi et al., 2006 Simple rules Physiological age of an individual in stage i at time t : Average duration in stage i : D For each individual, assumed known its stage i and its X tj j physiological age, X tj the age at time t t is given by: X j t t t j j X t max0; t t Dj j Mortality • Physiological/food dependent mortality • Predation mortality Mazzocchi et al., 2006 Simple rules For the adult stage (stage 6): qt X F n t where: q t = number of eggs produced by a female at time t tnL F f ( s )ds n t with L = female average life span f(t) = average reproductive profile of the female Mazzocchi et al., 2006 Optimal diet 3500 RESULTS Total population 3000 PRO ind. m-3 2500 2000 1500 1000 500 0 60 30 150 120 90 180 days 60 400 stage 5 350 stage 6 50 300 ind. m-3 ind. m-3 40 250 200 150 30 20 100 10 50 0 0 30 30 60 90 120 days 150 180 60 90 120 150 180 days Mazzocchi et al., 2006 The real world Mazzocchi et al., 2006 Message 17 Analyzing life cycle of key species may help understanding causative factors that modulate the cycle Synthesis 1. Marine environment is complex, dynamic and builds its own history 2. What part of the history we want to read depends on our priorities, but without events there is no history 3. Many events can be detected, monitored, characterized while they are occurring or reconstructed by their traces with very little effort 4. A huge amount of information has been accumulated in the last decades and is freely avaliable, given a web access 5. Helping in selecting priorities and sharing the information is the needed contribution we can provide