Long-term observations in coastal areas: a key tool for understanding the functioning of a system and detecting changes Maurizio Ribera dAlcal 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 Sampled fortnightly in and weekly from 1995 to present Napoli time series Marechiara Temperature 0-10 m Salinity 0-10 m Mixed Layer Depth (m) Monthly averages TIN 0-10 m SiO m PO m mol L -1 Monthly averages Int Chla 0-70 m mg m -2 Chla - 0 m mg m -3 The seasonal cycle of chl.a Phytoplankton - 0m Percentage Cells ml - 1 Abundance Biomass g C l -1 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 J F M 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 P. delicatissima 1 P. galaxiae 2 P. galaxiae 1 P. pseudodelicatissima P. subpacifica P. multistriata P. galaxiae 3 P. delicatissima 2 mol/dm 3 TIN Trophic conditions START OF THE BLOOMS P. delicatissima 1 P. galaxiae 2 P. galaxiae 1 P. pseudodelicatissima P. subpacifica P. multistriata P. galaxiae 3 P. delicatissima 2 DECLINE OF THE BLOOMS Max Min 75th % 25th % Median Zingone, Sarno, Nardella & Licandro, in preparation P. delicatissima 1 P. galaxiae 2 P. galaxiae 1 P. pseudodelicatissima P. frau/subfraudulenta P. subpacifica P. multistriata P. galaxiae 3 P. delicatissima 2 Max Min 75th % 25th % Median P. delicatissima 1 P. galaxiae 2 P. galaxiae 1 P. pseudodelicatissima P. frau/subfraudulenta P. subpacifica P. multistriata P. galaxiae 3 P. delicatissima START OF THE BLOOMSDECLINE OF THE BLOOMS C Zingone, Sarno, Nardella & Licandro, in preparation Temperature Chaetoceros throndsenii JJ 2001 JJM 2002 JJM 2003 JJM 2004 M 2005 FV NA PO FS FP PT PL cells ml Pseudoscurfieldia marina JJM 2002 JJM JJM M 2005 JJ 2001 Leptrocylindrus danicus AJ 2001 AJ 2002 AJ 2003 AJ 2004 Cerataulina pelagica AJ 2001 AJ 2002 AJ 2003 AJ 2004 cells ml Bacteriastrum furcatum MA 2002 MA 2003 MA 2004 MA Prorocentrum triestinum 2001 JMAJ 2002 MAJ 2003 MAJ 2004 MA 2005 MA 02030405 AMJ J 02030405 01 JJMJJ M 02030405 01 JA 01020304 JA 01020304 JJMJJ M 02030405 01 FV NA PO FS FP P PL Siano et al., in prep. Synchronous patterns in the occurrence of the species 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 g L -1 Chla 0m Salinity 0m PSU Looking for trends Message 10 Time series, even of basic hydrographic parameters highlight mid-term trends, if any Phytoplankton abundance cells ml -1 Looking for trends Message 11 Size may be an indicator of possible change in the structure of plankton community Factors/Issues Climate Change Atmospheric Deposition Basin Scale OscillationsAlien Species Land Based Inputs Seasonality Coherence Episodic vs. Chronic Hydrological Cycle (and flushing) Salinity and Precipitation Seasonal shift in wind direction Interannual variability in wind direction Nutrient load 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 2 sample Kolmogorov-Smirnov test Null Hyp.: Parameter true distrib. funct. is not > true distrib. funct Chl.a (0 m) is stochastically > Chl.a (0 m) p=0.003 Chl.a (0-70 m) is stochastically > Chl.a (0-70 m) p=0.03 S (0-10 m) is stochastically > S (0-10 m) p=0.06 Salinity and Chlorophyll a shifts T minStart of Stratification Max time for T- surf=III End low S 1984March IIApril IINovember IIJuly March IIApril IINovember IIJuly March IIApril IINovember IIJuly March IApril INovember IIJuly March IApril INovember IJuly II 1989March IApril IOctober IJuly I 1995April IINovember IJuly I 1996March IAugust II 1997March IMay IOctober IIJuly I 1998March IIApril IIOctober IIJuly I 1999February IIApril IIIOctober IIIJuly I March IMay INovember IIIAugust I 2002January IApril IOctober IIIJuly I 2003March IIApril IIOctober IIgiugno I 2004February IApril IINovember IIJuly III Phase variability (decade of the month) Phaeocystis sp.Protoperidinium diabolus Thalassiosira rotula Calciopappus caudatus Idealized shapes 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 Total copepod abundance Size (ESD) Number Copepod size spectrum Diversity of size classes Shannon index (Parson, 1969, Ruiz, 1994) Digitalisation of >600 samples with the ZOOSCAN 1 Size class ~ 1 Species 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 ( cellsl -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 Intense monitoring activities of causative organisms and/or toxins Zingone & Wyatt, 2006 Responses to HABs 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 9697989900010203040506 Time P. multistriata cell size distribution Percentage of cells Size classes (m) Cell/ml DAlelio et al., in prep. An intriguing species a bc Cell size (m) % DAlelio et al., in prep. Possible scenarios Cell size (m) Time DAlelio et al., in prep. Model for hindcasting Message 16 Very simple models integrate observations and help in testing hypothesis on mechanisms 1984 85 86 87 88 89 90 97 98 99 A highly seasonal species Mazzocchi et al., 2006 Mean seasonal cycle of T. stylifera at st. MC juv. A highly seasonal species Mazzocchi et al., 2006 THE MODEL STRUCTURE eggs NI NII-NVI CI CII-CV adults (f, m) stages Mazzocchi et al., 2006 Another hindcasting model Mortality Physiological/food dependent mortality Predation mortality For each individual, assumed known its stage i and its physiological age, the age at time is given by: Physiological age of an individual in stage i at time t : Average duration in stage i : Simple rules Mazzocchi et al., 2006 For the adult stage (stage 6): where: = number of eggs produced by a female at time t with L = female average life span f(t) = average reproductive profile of the female Mazzocchi et al., 2006 Simple rules stage 5 Total population PRO RESULTS stage 6 Mazzocchi et al., 2006 Optimal diet 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