Comparative analyses of marine microbial systems: searching for pattern

1. Comparative analyses of marine microbial systems: searching for pattern Carlos M. Duarte IMEDEA (CSIC-UIB), Spain Microbial Oceanography: Gemones to Biomes Univ. Hawaii, 8 July, 2008

2. Microbial Oceanography still on a exploratory phase: • Most of the ocean’s biota undersampled (particularly Southern Hemisphere, depths > 200 m, and depths < 3 m) • Still discovering key players in the ocean. • Still discovering new key metabolic pathways in the ocean. • New habitats discovered: hydrothermal vents, whale carcases, sea mounts, deep corals, etc. • Starting to appreciate spatial variability but very limited insights into temporal variability, while the ocean is changing dramatically without waiting for us to get our “steady state” picture right. “The most general problem of marine biology is to understand the distribution and abundance of life in the sea. The approach to this problem must be through the development of statistical relationships between large quantities of observations on biological and physical events occurring in widely scattered places” (Redfield 1958)

3. Comparative approaches “The most general problem of marine biology is to understand the distribution and abundance of life in the sea. The approach to this problem must be through the development of statistical relationships between large quantities of observations on biological and physical events occurring in widely scattered places” (Redfield 1958) The Key to this lecture

4. Comparative approaches “The most general problem of marine biology is to understand the distribution and abundance of life in the sea. The approach to this problem must be through the development of statistical relationships between large quantities of observations on biological and physical events occurring in widely scattered places” (Redfield 1958)

5. Comparative approaches “The most general problem of marine biology is to understand the distribution and abundance of life in the sea. The approach to this problem must be through the development of statistical relationships between large quantities of observations on biological and physical events occurring in widely scattered places” (Redfield 1958)

6. Comparative approaches “The most general problem of marine biology is to understand the distribution and abundance of life in the sea. The approach to this problem must be through the development of statistical relationships between large quantities of observations on biological and physical events occurring in widely scattered places” (Redfield 1958)

7. The goal of science What is the nature of science ? Understanding ? Explanation ? Discovery ? Prediction ? • Bacon: “The role of science is the search for the causes of the different phenomena”. Explanation is the goal of science • Science: the accumulation of knowledge ?

8. What is understanding? Chinese tide tables accurately predicted tides more than 1,000 years ago, although understood the process as the “breading” of the oceans. We now perfectly “understand” that it is results from multiple gravitational forces…. But use empirically-fitted curves to construct tide tables! (just as in the Chinese tide tables) Sishi Chaohou Tu ( 1056)

9. What is understanding? Scientific theories are perishable constructs to be challenged and replaced by others most consistent with the observations available “Discovery consists in seeing what everyone else has seen and thinking what no one else has thought” Albert Szent-Gyorgyi, Nobel Laureate

10. Comparative approaches in the Petabyte Age: Massive availability of data – The Petabyte Age – is now leading to analyses of massive data sets as the dominant pathway for scientific progress – doing away with Theory. e.g. The Celera Genomics project for the Human Genome succededed largely because of a differential impetus to mass data analysis relative to the competing project (driven by brute data analyses rathern than understanding) – and C. Venter has continued to extend genomics beyond understanding by mass sequencing. In th Petabyte Age correlation and statistical analyses of massive amounts of daa are superseeding causation and science can advance without theories and understanding.

11. The goal of science An alternative view • “The goal of marine biology is to (predict and) understand the distribution and abundance of life in the sea” (Redfield 1958) • “Ecology is the branch of science that predicts the distribution, abundance, biomass, and kinds (sizes, ecotypes, species...) of organisms” (Peters 1980) • “Biological oceanography is the branch of environmental science whose goal is to predict what kinds of organisms will be found in what abundances where and when within the sea” (Jumars 1993)

12. The goal of science Fact: an observation, a datum. It has not yet been interpreted and no scientific claim on it has been done. (although any observation is framed in our background...). Theory: a generalization that goes beyond the facts. It makes predictions: statements about facts we have not yet observed. Any device that makes predictions Prediction: any falsifiable statement about some aspect of the universe. Induction is the process by which we create theories out of facts. The way we move from a set of facts to a theory of generalization. Deduction the process by which we issue a particular prediction from a general theory

13. To predict vs. to understand The arch of knowledge Theories Laws of nature hypotheses synthesis Relevant aspects Predictions increasing generality analysis Observations inductive ascent deductive descent

14. The role of different approaches A rejoinder The comparative approach allows for exploratory analysis and can therefore generate useful knowledge in research programs too undeveloped to allow the formulation of non-trivial hypotheses. In contrast, experimental and modelling approaches are hypotheses or process-driven and cannot be effectively used in new research questions. Comparative analyses provide a robust empirical basis (“laws of nature”) to develop explanatory theories, leading to experimental tests, which, in turn, suggest new relevant comparisons.

15. Scientific knowledge Scientific theories are intelectual constructs that allow falsifiable predictions about the subject of the theory. A theory should be considered scientific if and only if it is falsifiable. Karl Popper (1902 – 1994)

16. 4 3 2 1 1 2 3 4 The goal of science How fast do babies grow ? Facts Weight of baby (kg) Age of baby (months)

17. 4 4 3 3 2 2 1 1 1 2 3 4 1 2 3 4 The goal of science How fast do babies grow ? Facts Two competing theories W = e (0.346 X) W = X Weight of baby (kg) Age of baby (months) Age of baby (months)

18. 4 4 3 3 2 2 1 1 (3, 4) 4 3 Weight of baby (kg) 2 1 1 2 3 4 Age of baby (months) The goal of science How fast do babies grow ? Facts Two competing theories W = e (0.346 X) W = X Weight of baby (kg) Test

19. The goal of science In the realm of the possible... …a theory extracts the range of the probable

20. The goal of science In the realm of the possible... ...this is the unexpected

21. log R = 0.62 log P + 0.01 Empirical models (approaches, patterns...) = Comparative models (approaches, patterns...) Empirical Models Most models are at a minimum, semi-empirical, as they contain statistically-fitted elements Robinson and Williams (2005) Allow quantitative tests of specific hypotheses and reveal patterns that may represent basic ecological laws

22. Empirical Models • Focus on a distinct process or attribute to describe patterns in that attribute among different systems • usually, but not necessarily, tend to be synoptic analysis of many systems; • often involve statistical analysis of empirically determined, quantitative patterns • frequently use natural experiments to identify patterns along gradients or across contrasts (but this does not preclude the possibility of laboratory study, field experiment, or mesocosm tests) • and many analysis involve simple, well-defined variables (eg. temperature), aggregate properties (e.g. chlorophyll) and relatively large systems (eg. ecosystems).

23. All Models are Wrong, but some are useful George E.P. Box (1919- )

24. The role of different approaches Comparative research • Limitations: • Often flawed by poor precision (order-of-magnitude resolution) • The data available do not represent random samples • Dependent on the existance of a sufficient empirical basis (data set). • Uncertainty as to underlying mechanisms may lad to spurious expectations • Often confounds the effects of time and space: too dependent on the assumption of direct relationship between forcing and response variables. (This brings disagreements between scientists focused at different levels). • Because a number of factors may underly natural variability, comparative analysis can only suggest explanations • Bounded by the particular scale of the analysis (i.e. cannot be extrapolated) because there's no guarantee of uniformity in nature.

25. The role of different approaches Modelling approaches • Excellent tools for heuristic exploration of ideas (what if...) and artificial scenarios. • Help articulate current knowledge. • They can reject inconsistent hypothesis even in data poor situations • Many possible approaches depending on amount of data and knowledge • Limitations: • Very demanding on high quality data and knowledge (not well suited for new research programs) • Reproducing the functioning of nature cannot be a goal for this leads to the development of over-complicated models. (Precision of output declines parallel to the increase in the number of functions) • Deterministic models soon lose this quality when interactions, particularly non-linear ones, are considered • Cannot incorporate the unknown

26. One caveat… Is prediction possible in Earth Science models? ( Mathematical characterization of the significant components of a system and the interactions of these components, to yield a quantitative predictive model) It has been argued that it is not: “…even if a model result is consistent with the present and past observational data, there is no guarantee that the model will perform at an equal level when used to predict the future. First, there may be small errors in input data that do not impact the fit of the model under the time frame for which historical data is available, but which, when extrapolated over much larger time frames, do generate significant deviations. Second, a match between model results and present observations is no guarantee that future conditions will be similar, because natural systems are dynamic and may change in unanticipated ways” Oreskes N, Shrader-Frechette K, Belitz K. Verification, validation, and confirmation of numerical models in the earth sciences. Science 1994;263:641–6.

27. The role of different approaches Microbial Oceanography • A time of rapid discovery (organisms: Picoautotrophs in the 1980’s, Archaea, New metabolic pathways: ANAMOX1, Anoxygenic photosynthesis, etc.; New Methods). • Not yet ripe for knowledge-demanding modelling approaches. • A promising field for exploratory analyses. 1 Anaerobic ammonium oxidation

28. Taxonomy Methods Abundance & biomass Production & growth Trophic interactions Nutrients Geochemical effects 0 20 40 60 80 100 120 140 0 50 100 150 200 250 Allocation of effort to different subjects and approaches in Aquatic Microbial Ecology Comparative Models Experimental Descriptive Number of papers published (1990-1995) Number of papers published (1990-1995) Number of papers published (1990-1995) Number of papers published (1990-1995) from Duarte et al. 1997

29. 0 100 200 300 400 500 600 A new journal L&O Methods created in 2003 (Paul Kemp, Editor) Hobbie et al. (1977) Methods Methods Porter & Feig (1980) Methods Methods Azam et al. (1983) Concepts Concepts Fuhrman & Azam (1982) Methods Methods Cole et al. (1988) Comparative Comparative Simon & Azam (1989) Methods Methods Fuhrman & Azam (1980) Methods Methods Proctor & Fuhrman (1990) Concepts Concepts Bergh et al. (1991) Concepts Concepts Cho & Azam (1988) Concepts Concepts Citations (92-95) from Duarte et al. 1997

30. Building comparative analyses What do people use ? Methods used in variable-focused interecosystem comparisons published during 1988 and 1989 in Ecology and Oecologia (Downing 1991) Method Frequency (%) Regression (bivariate, multivariate, nonlinear) 41 Inspection of graphs 31 Inspection of means and confidence intervals 24 ANOVA (parametric or nonparametric) 18 t-tests 15 Correlation 13 Inspection of tables 13 Chi-square analysis 13 Paired tests (parametric) 6 Nonparametric random sample tests 5 Paired tests (nonparametric) 3

31. Building comparative analyses • Identifying the scales of variability • One of the goals of comparative ecology is to describe the range of states possible for a property by quantifying its variability • Ecologists seek to extract patterns or trends (useful structure) from that variability • The identification of pattern require that the structure of the variance be recognized. • Variability may differ from one level of observation to another. Error Random Pattern Variance

32. 60 50 40 30 20 10 0 2-5 10-15 20-25 30-35 40-45 > 45 Building comparative analyses • Gathering the data • a) Where to find the data ? • Use the literature • Add some of your own • Know how people got the data • b) How many data ? • Empirical basis as wide as possible • Range is important. So try to use the extremes • Samples are usually not at random • How many ecosystems ? • c) How many “X” variables ? • With 20 ind. variables, there are 1048575 multiple regression models possible. At p: 0.05, 52428 will be significant by chance alone !! Number of ecosystems analyzed per paper (Downing 1991) Frequency

33. 2. A review of Comparative Analyses • A. food-web structure (abundances, biomasses, body size...) • Prokaryote concentration, size and biomass • Viral concentration • Heterotrophic protists concentration • Pico- and nanoalgal concentration • B. food-web dynamics (production, activity, respiration, grazing rates...) • Prokaryote activity, production, growth efficiency and growth rate • Algal production and growth rates • Viral production rates • Protist grazing rates and growth rates • C. community structure and dynamics • Biomass structure • Ecosystem function • Community metabolism

34. Bacterial concentration, size & biomass • Aizaki et al. 1981 For the first quantified relationship CHL-BA • Bird & Kalff 1984 For the first study of the implications • Cota et al. 1990 For an analysis of the temporal/spatial coupling • Currie 1990 Nutrient limitation and the CHL-BA relationship • Cho & Azam 1990 No relationship in oligotrophy ? • Simon et al. 1992 Differences in freshwater and saltwater • Li et al. 1992 Less steep relationship • Buck et al. 1996 Even less steep relationship ! • Zinabu & Taylor 1997 Salt lakes • Sommaruga & Robarts 1997 Hypertrophic systems • Basu & Pick 1997 For the study of rivers • Billen et al. 1990 Relationship bacterial size-trophy • Rivking and Legendre (2002) Bacterial biomass vs. Latitude • Smith and del Giorgio (2003) Fraction of active bacteria

35. Viral abundance • Paul, Cochlan,, Boehme et al.1993 Firsttrends in relationshipwith CHL • Jiang & Paul 1994, Paul et al 1993 Negativetrendswithsalinity • Maranger & Bird 1995 GoodrelationshipwithChla • Slightchangeofthe VBR withtrophy • Somechangewiththesystem: VBR is 1.7 times higher in freshwater • Weimbauer & Suttle 1997 Higherslopesthanthose in M&B • Weimbauer et al 1993 Trends in VIB • Pedrós Alió et al. (2000) Significantdifferencesbetweenmethods • No differencesbetweenfreshwaterand marine • Trends in the VIB • Bacteriovory more importantthan viral lysis

36. Heterotrophic protists Predator vs. prey abundance • Sherr et al.’84, Davis et al.’85 Evidence of good relationship BT-HNF • Pace’86, Beaver & Crisman’89 Good relationship Ciliates - CHL • Berninger et al.’91, Sanders et al.’92 Tight relationship BT-HNF • Gasol & Vaqué 1993 Not that tight ! Other factors are important • Gasol et al. 1995 Analysis of some of these factors • Basu & Pick 1997 BT - HNF in rivers • Suzuki et al. 1998 Nanociliates - CHL in the Pacific • Pedrós Alió et al. (2000) Significant differences between methods • Trends in the VIB • Bacteriovory more important than viral lysis

37. Pico- and nanoalgal abundance • Watson & Kalff '81; McCauley et al.'88; Watson & McCauley'88 Different pattern of variation of nanoplankton and net plankton (edible/inedible) • Søndergaard'91; Burns & Stockner '91 Dominance of picoplankton in oligotrophic lakes... • Søndergaard et al.'91 ...as well as in the Baltic • Stockner & Shortreed 1991 Role of TP and pH in picoalgae dominance • Duarte et al. 1992 Biomass share in Florida lakes • Seip & Reynolds 1995 Surface models along season and trophy • Buck et al. 1996 Picoalgae and latitude (temperature) • Agawin et al. (2000) Picoautotroph abundance related to community biomass, temperature and nutrients • Li (2002) Picophytoplankton abundance and diversity vs. • Cell size and stratifications.

38. Bacterial activity, production & growth • Cole et al. 1988 Relationship BP - PP • White et al. 1991 Trends BP and SGR withAbundance, CHL, • PP andtemperature • Sommaruga & Robarts 1997 Trendsof SGR with CHL in eutrophiclakes • del Giorgio & Scarborough 1995 Active bacterial abundancechangewithtrophy • del Giorgio et al. 1997a Active bacterial abundanceand BP • del Giorgio et al. 1997b Bacterial respiration vs. BP and PP • del Giorgio & Cole 1998 Bacterial growthefficiency as itchangeswith BP substrateC:Nand PP • Christian & Karl 1995 Enzymaticactivitiesandtemperature • Sanders et al. 1992 Bacterial growthandlossrates • RivkinandLegendre (2001) Bacterial growthefficiency vs. T • RivkingandLegendre (2002) Bacterial respirationandproduction vs. latitude

39. Algal and viral activity Pico- and nanoalgal production and growth • Smith 1979 Relationshipbetween CHL and PP in lakes • Lafontaine & Peters 1986 Similar in the sea • Platt et al.1992 Nutrient control ofphotosynthesis in theocean • Agawin et al. 1998 Relationship SGR andtemperature • Agawin et al. (2000) Picoautotrophproductionrelatedtocommunityproduction, temperature • Andnutrientconcentration Viral production • Steward et al. 1992 Trends in Viral productionrates • Suttle (1994) Bacterial infectionandeffectsonproduction • Parada et al. (2006) Burstsize vs. Trophic status, % infection, • Bacterial production

40. Heterotrophic protists activity & growth • Peters 1994 Labandfieldstudies. Modelswithsize, • temperatureandconcentrations • Vaqué et al. 1994 Methodologicaldifferencesandmodelswithabundancesandtemperatures (field) • Nielsen & Kiørboe 1994 Growthrate vs. Temp. andsize, appliedtofield data • Pérez et al. 1997 Differentgrowthratesofmixo- andheterotrophs • Sanders et al. 1992 Bacterial growthandlossrates • CalbetandLandry (2004) MicrozooplanktonGrazingrates. • LandryandCalbet (2004) Microzoplanktonproductionrates

41. Biomass structure • Cho & Azam’90, Simon et al’92 Bacteria dominate biomass structure in the ocean • del Giorgio & Gasol 1995 Ratio H/A with CHL in lakes Heterotrophic biomass as related to total food mass • Gasol et al. 1997 Ratio H/A in marine systems related to algal SGR ? • Sterner et al. 1997 Relationship between available light:P and seston composition

42. Ecosystem function • Baines & Pace 1991 EOC release as a function of PP • Baines et al. 1994 Why sinking rate is different in lakes and oceans ? • del Giorgio & Peters 1993 How are Total respiration rates and PP related to trophy • del Giorgio & Peters 1994 Same, plus a study of the effect of allochthonous C • del Giorgio et al. 1997 Bacterial respiration as related to PP • Williams 1998 Oxygen production vs. oxygen consumption • Duarte & Agustí 1998 Community respiration vs. GPP • Del Giorgio and Cole 2000 Bacterial growth efficiency • Morán et al. (2002) DOC release and baceria vs. Phytoplankton coupling

43. CHL BA TP PP BP Temp 0 10 20 30 40 50 60 70 80 Comparative analysis of the comparative analyses Explanatory variables Number of equations

44. 8 8.5 7.5 8 7 7.5 6.5 6 7 5.5 6.5 5 6 4.5 4 5.5 -0.5 0 0.5 1 1.5 2 2.5 3 -2 -1 0 1 2 3 Comparative analysis of the comparative analyses Bacterial abundance Bacterial abundance Log Total Phosphorus Log Chlorophyll a

45. 9 9 8.5 8.5 8 8 7.5 7.5 7 7 6.5 6.5 6 6 5.5 5.5 5 5 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 Comparative analysis of the comparative analyses Viral abundance Viral abundance Log Chlorophyll a Log Bacterial abundance

46. 3 4 2.5 3 2 1.5 2 1 1 0.5 0 0 -0.5 -1 -1 -1 0 1 2 3 4 -2 -1 0 1 2 3 6 5.5 5 4.5 4 3.5 3 2.5 2 4.5 5 5.5 6 6.5 7 7.5 8 8.5 Comparative analysis of the comparative analyses Bacterial production Bacterial production HNF abundance Log Primary Prod Log Chlorophyll a Log Bacterial abundance

47. Comparative analysis of the comparative analyses Log-log Relationships N Slope SE Bacterial abundance - Total Phosphorus 11 0.60 0.07 Bacterial abundance - Chlorophyll 36 0.47 0.03 Bacterial abundance - Bacterial production 10 0.48 0.05 Viral abundance - Chlorophyll 6 0.66 0.09 Viral abundance - Bacterial abundance 7 1.01 0.20 HNF - Bacterial abundance 8 0.63 0.15 Bacterial production - Chlorophyll 5 0.71 0.14 Bacterial production - Bacterial abundance 6 1.23 0.12 Bacterial production - Primary production 5 0.67 0.11 Primary production - Chlorophyll 7 1.03 0.13 Respiration - Primary production 9 0.68 0.10

48. Y > 1 Y increases faster than X (Y/X increases with X) Y = 1 Y proportional to X (Y/X independent of X) Y < 1 Y increases slower than X (Y/X decreases with X) What does the slope mean? Variable Y Variable X

49. Comparative analysis of the comparative analyses Log-log Relationships N Slope SE Bacterial abundance - Total Phosphorus 11 0.60 0.07 Bacterial abundance - Chlorophyll 36 0.47 0.03 Bacterial abundance - Bacterial production 10 0.48 0.05 Viral abundance - Chlorophyll 6 0.66 0.09 Viral abundance - Bacterial abundance 7 1.01 0.20 HNF - Bacterial abundance 8 0.63 0.15 Bacterial production - Chlorophyll 5 0.71 0.14 Bacterial production - Bacterial abundance 6 1.23 0.12 Bacterial production - Primary production 5 0.67 0.11 Primary production - Chlorophyll 7 1.03 0.13 Respiration - Primary production 9 0.73 0.10

50. Exploring implications of the patterns Slopes heterotrophs vs. autotrophs are < 1 Equivalent patterns in the ratio Respiration : Production ? H / A is > 1 in oligotrophy Are heterotrophs maintained by higher autotroph’s SGR in oligotrophy ? What maintains heterotrophs in oligotrophic oceans ? Do heterotrophs regulate autotrophs in oligotrophy ?