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Science the hell out if - K. Stergiou
The document discusses the application of various data analysis models to enhance understanding of marine ecosystems, including the impacts of climate change on fish populations in the Mediterranean. It highlights the importance of time series data for predictive modeling and emphasizes the need for simple, explanatory models in ecology. Additionally, it presents specific case studies and frameworks for analyzing fisheries data and habitat suitability to address current ecological challenges.
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Science the hell out if - K. Stergiou
- 1. Science the hellout of it Kostas Stergiou Institute of Marine Biological Resources and Inland Waters, Hellenic Centre for Marine Research, Athens, Greece School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
- 2. Shiny new insightsBigOLd (and new) Data for what is not directly observed Anchovy Spawning groundsClimate models Niche models IBM GLMs, GAMs Classification Tree Analysis Random Forests Boosted Regression Trees Multivariate Adaptive Regression Splines Surface Range Envelope and eventually to knowledge information
- 3. One important issueis that Big OLd (and new) Data (BOLD) allow us to construct cheap time series… A series of measurements of a variable at equal time intervals and the usual notation is Yt, for t = 1, 2, 3,…, K
- 4. 0 200 400 600 800 1000 1200 1900 1920 19401960 1980 2000 Year Zooplanktonbiomass Why time series? Because one measurement in time is not enough …
- 5. More points …better … … but still not enough … 0 200 400 600 800 1000 1200 1900 1920 1940 1960 1980 2000 Year Zooplanktonbiomass Estimate of mean and variance
- 6. Increasing the timehorizon reveals many interesting features … 0 200 400 600 800 1000 1200 1900 1920 1940 1960 1980 2000 Year Zooplanktonbiomass Missing point Gap Unusual event or something went wrong …Cycles and reveal the invisible present
- 7. Ecology should predict Simple models Explanationsat two levels: Proximate level (what factors are responsible …) Ultimate level (evolutionary advantage) Scientists should: • end up with simple, predictive models • that have explanations at both levels (proximate, ultimate) Thus, BOLD lead to shiny new insights that hopefully…
- 8. Scientists will beable to e.g. prove that what people think is wrong (see Davis 1971) … and allow us to handle the ‘so what’ question…
- 9. Let see several examplesfrom various disciplines, from fish, fisheries and marine ecosystems to academia
- 10.
- 11. Research/Information tool www.fishbase.org 1989 1995 1998 First publicationciting FishBase Froese (1990) Fishbyte FishBase is the modern tool …
- 12. Widened the scopeof fisheries science Global studies Κnowledge Ιnformation framework for answering high- order questions 0 500 1000 1500 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Length (log; cm) Frequency Upscaled fisheries science Stergiou (2003)
- 13. Parameter Biological Oxygen consumption Gill surface Brainsize Body girth Tail surface Mouth area Swimming speed Acceleration Manoeurability (i.e. turning rate, radius, angle) Demographic Age Weight Length at maturity Age at maturity Fecundity von Bertalanffy K Ecological Mortality Trophic level Prey length Fisheries Length at capture Length at optimum exploitation Management Economic value Sensitization of public Body length (log or not) Parameter(logornot) FishBase - Quantification of the biology and ecology of fishes This is true Length is the most important parameter related to a variety of other parameters
- 14. Predict the value ofother parameters for rare or not studied species Length From existing relationships Otherparameter FishBase Patterns and propensities in fish biology and ecology Quantification Results of Bsc, MSc or PhD theses
- 15. 2 Climate change impactson Mediterranean food-web structure
- 16. Use of thecoupling of species distribution and trophic models for predicting climate change impacts on food-web structure across the Mediterranean. The authors propose a new framework
- 17. They used datafrom the Mediterranean continental shelf: • published food webs • stomach contents from literature and FishBase • actual geographical distributions (occurrence maps) for 256 endemic and native coastal fish species • Sea Surface water Temperature (SST) as the main forcing variable • the Mediterranean regional marine model (NEMOMED8) that predicts observed and future SST based on a variety of drivers • projected SST values were extracted for 2080–2099 from NEMOMED8 outputs, based on the SERS IPCC A2 scenario • this scenario is conservative but not the most pessimistic.
- 18. number of feeding linksbetween fish species would decrease on 73.4% trophic level Vulnerability, the mean number of consumer species per prey species, would decrease Differences in various attributes between 1961–1980 (baseline scenario) 2080–2099.
- 19. 3 Annual probability mapsand habitat allocation of species
- 20. 20 A series ofsolid work done within the framework of several projects of IMBRIW with the participation of the different “Small pelagic” research groups in the Mediterranean EU FUNDED project, FP6 Framework MARIFISH WP7: Collaborative Research Programmes Regional Scale Study-The Mediterranean MEDISEH DG MARE Contract Service REPROdUCE: MARIFISH Framework IEO IOF • Mediterranean Acoustic Survey since 2008 • Internationally coordinated • Simultaneous hydrographic sampling Annual probability maps and habitat allocation of the main small pelagic species
- 21. Data used todevelop habitat maps: Biological data (at different seasons/years/areas): • Acoustic surveys (MEDIAS) • Ichthyoplankton surveys • Pelagic trawl data Environmental data (satellites): • Sea level anomaly • SST, depth • Chl-a • Salinity • Photosynthetic active radiation 21
- 22. Statistical models + Habitat mapsat the Mediterranean scale Biological & environmental data SST inSST in ooCCSST inSST in ooCC 22o 23o 24o 25o 26o 27o 38o 39o 40o 41o 2004 22o 23o 24o 25o 26o 27o 38o 39o 40o 41o 22o 23o 24o 25o 26o 27o22o 23o 24o 25o 26o 27o 38o 39o 40o 41o 2004 -5 0 5 10 15 20 25 30 35 40 30 35 40 45 0.25 to 0.5 0.51 to 0.75 0.751 to 1 This has been done (in different publications) for: • Anchovy • Sardine • Mediterranean horse mackerel • Mackerel
- 23. Practical uses ofhabitat suitability maps 23 Evaluate existing FRAs /MPAs & Define new FRAs/MPAs Provide input to IBMs & Examine climate change scenario Set a framework to minimize discards H2020 MINOW Provide input to Ecosystem models with spatial perspective Covariates in other habitat suitability models
- 24.
- 25. 3858 recipes No vulnerable,low popularity in recipes vulnerable, popular in recipes Numberofrecipes Vulnerability
- 26.
- 27. Tenure leads todecreased productivity…. True?
- 28. Number of publications isan index of productivity Mean number of publications (N) of 2136 profs from 123 Universities from 15 countries N 1996 2014 1996 2014 7 2 7 2 r2=0,95, p<0,0001 r2=0,97, p<0,0001 r2=0,92, p<0,0001 r2=0,81, p<0,0001 Wrong !!!!!!!!!!
- 29.
- 30. Fame is whatit is known about a name. Fame can be estimated ... How? From the frequency a name e.g. appears in a book… What is the most famous fish?
- 31.
- 32. Ένα χρυσόψαρο μέσαστη γυάλα και μια γατούλα μούρλια θηλυκό πρασινομάτα και κοκκινομάλα αρχίσανε ένα παιχνιδάκι ερωτικό ... Από την ταινία Το Κοροϊδάκι της δεσποινίδος (1960)
- 33. Frequency of commonnames goldfish, rainbow trout, swordfish, Atlantic salmon, guppy English books 1800-2000. 0 120 Fame(infamons) 60 Year 1800 1840 1880 1920 1960 2000 Swordfish Σολωμός Guppy Rainbow trout Year Fame Goldfish has penetrated in all aspects of cultural life and Darwin has a chapter dedicated to it…
- 34. (Darwin 1868, p.296-297)
- 35. 7 Fisheries and thecheapest information available …
- 36. 0 100 200 300 400 500 600 1928 1930 19321934 1936 1938 Year Landingsint Annual landings (in t) of cephalopods, 1928-1939 (data from NSSH) 400 500 600 700 192819291930193119321933193419351936193719381939 Year Numberoftrawlers Although the NSSH officials told me that there are no data prior to 1964 …. ….. But soon I found NSSH data for effort and landings during 1928- 1939 !!!!!!! (c) 0 2000 4000 6000 8000 10000 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 Year Cephalopodlandings(int) 0 2000 4000 6000 8000 10000 1928 1930 1932 1934 1936 1938 Year Cephalopodlandings(t)
- 37.
- 38. And the listgoes on …
- 39. Climate models Niche models IBM GLMs,GAMs Classification Tree Analysis Random Forests Boosted Regression Trees Multivariate Adaptive Regression Splines Surface Range Envelope our ecoscope
- 40. Thank you foryour time Ευχαριστώ για το χρόνο σας