Experimental design & Physiological endpoints
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1 Sam Dupont Researcher, Associate Professor University of Gothenburg 杜邦憲 Assistant Professor University of Hong Kong Visiting scholar University of California, Berkeley Experimental design & Physiological endpoints
2 On the menu 1. How to design a relevant biological experiment? 2. Physiological endpoints (case: larval stages)
3 On the menu 1. How to design a relevant biological experiment? 2. Physiological endpoints (case: larval stages)
4 Take home messages Every experiment is an abstraction of reality There is nothing like a perfect experiment! Essentially, all models are wrong, but some are useful Essentially, all experiments are wrong, but most are useful George E. P. Box Be aware and honest about your limitations
5 Trade-offs Realism [duration, tested parameter, environment, etc.] vs. Feasibility [manpower, money, space, time]
6 How to design your experiment 1. What is your question? Your hypothesis? 2. How can I test this? - What are my limitations? - What is the best model? - What are the best endpoints? - What are the best design/stats? - What are my controls? - etc. Can I REALLY answer my question with the collected data?
7 How to design your experiment 1. What is your question? Your hypothesis? 2. How can I test this? - What are my limitations? - What is the best model? - What are the best endpoints? - What are the best design/stats? - What are my controls? - etc. Can I REALLY answer my question with the collected data?
8 What is your question? Read the literature! or ask experts not only OA literature [Theoretical background, methods, etc.] Standing on the shoulders of giants
9 Key challenges in OA Evolution Ecology Multiple drivers Variability
10 What is your question? Read the literature! (or ask experts) not only OA literature [Theoretical background, methods, etc.] Exploration is fine BUT do not stamp collect Do not base your experiment on a technique [e.g. -omic revolution ]
11 Impact of OA on sea urchin larvae CONTROL vs ACIDIFICATION 1 time point Technically sound Conclusion???
12 How to design your experiment 1. What is your question? Your hypothesis? 2. How can I test this? - What are my limitations? - What is the best model? - What are the best endpoints? - What are the best design/stats? - What are my controls? - etc. Can I REALLY answer my question with the collected data?
13 e.g. TRUE replication
14
15 e.g. replication # Parameters # Treatments # replicates # tanks 1 x 2 x 2 = 4 1 x 2 x 4 = 8 1 x 4 x 4 = 16 2 x 4 x 4 = 32 3 x 4 x 4 = 48
16 Practical limitations
17
18 How to design your experiment 1. What is your question? Your hypothesis? 2. How can I test this? - What are my limitations? - What is the best model? - What are the best endpoints? - What are the best design/stats? - What are my controls? - etc. Can I REALLY answer my question with the collected data?
19 August Krogh (Krogh 1904) Krogh s principle For such a large number of problems there will be some animal of choice, or a few such animals, on which it can be most conveniently studied
20 The top model - Biological feature (e.g. life cycle, generation time) - Ecological / Economical importance - Tools available (e.g. functional methods, genome) - Charismatic species - etc.
21 The top model (Dupont et al. 2014)
22 How to design your experiment 1. What is your question? Your hypothesis? 2. How can I test this? - What are my limitations? - What is the best model? - What are the best endpoints? - What are the best design/stats? - What are my controls? - etc. Can I REALLY answer my question with the collected data?
23 Endpoints? Fitness (e.g. survival, growth, reproduction) Physiology energy budget (e.g. respiration, feeding, excression, calcification) Etc. etc. Question on methods, ask us!
24 Best endpoints? - Not the coolest method - Not the most familiar method - As close to function as possible (e.g. fitness) Feder & Walser 2005
25 Changes bad We like bad news Negative effect: 9.8 citations / year Positive/neutral effect: 6.2 citations / year A change in your proxy change in fitness
26 Fitness is relative (interpretation) Single Multi Single Control = = = -0.4 (-)
27 How to design your experiment 1. What is your question? Your hypothesis? 2. How can I test this? - What are my limitations? - What is the best model? - What are the best endpoints? - What are the best design/stats? - What are my controls? - etc. Can I REALLY answer my question with the collected data?
28 How to design your experiment ANOVA design Little predictive power Scenario 1 Scenario 2 Scenario 3 n=x n=x n=x Regression design Need to have a relationship
29 ANOVA vs. Regression (Dorey et al. 2013)
30 Sampling strategy Frequency (more = more chance to identify effects & interactions) (Stumpp et al. 2011)
31 How to design your experiment 1. What is your question? Your hypothesis? 2. How can I test this? - What are my limitations? - What is the best model? - What are the best endpoints? - What are the best design/stats? - What are my controls? - etc. Can I REALLY answer my question with the collected data?
32 Stress ecology - niche (Van Straalen 2007) Need to understand the biology of your species
33 On the menu 1. How to design a relevant biological experiment? 2. Physiological endpoints (case: larval stages)
34 Example Question: What is the impact of OA on larvae? (multiple drivers, variability) Model: sea urchin Hypothesis: Interaction can be predicted by mode of action Strategy: Monitoring, mechanistic understand, field and lab experiments, models Societal relevance: Predictive model, mapping
35 An impossible task? It is NOT possible to test ALL species/ecosystems, in ALL locally relevant conditions including LOCAL variability (today and future) We need to understand the mechanisms Dupont & Pörtner (2013) Nature
36 E.g. organism response to multiple drivers 1. Good data on local variability / future scenarios 2. Good understanding of biological response for each driver [mode of action] 3. Build models Mix all the ingredients & test using scenarios [field, laboratory]
37 Importance of sorting stressors Driver A Driver B (similar mode of action) Driver C (different mode of action)
38 Keystone species Commercially important Genome available + GRN Functional tools Centuries of data (model) Sensitive to acidification Sea urchin
39 E.g. organism response to multiple drivers 1. Good data on local variability / future scenarios 2. Good understanding of biological response for each driver [mode of action] 3. Build models Mix all the ingredients & test using scenarios [field, laboratory]
40 1. Good data on local variability / future scenarios (Dorey et al. 2013)
41 E.g. organism response to multiple drivers 1. Good data on local variability / future scenarios 2. Good understanding of biological response for each driver [mode of action] 3. Build models Mix all the ingredients & test using scenarios [field, laboratory]
42 Growth Delay in development e.g. Martin et al. (2011) J. Exp. Biol; Stumpp et al. (2011a,b) Comp. Biochem Physiol
43 Dissecting the energy budget Increased respiration Stumpp et al. (2011a,b) Comp. Biochem. Physiol.
44 Feeding physiology Ingestion/Digestion rates ph in the digestive track Enzymatic activity Cellular structure Stomach is alkaline Compensation mechanisms Extra costs
45 Acid-base regulation A B 45 Pa CO2 R = PBC ph NBS Pa CO Pa CO acute hypercapnia chronic hypercapnia seawater ph NBS
46 Acid-base regulation No phe regulation phi regulation Role of HCO3 -, H + -pumps Extra costs
47 Acid-base regulation Key role of H+-pumps (Dupont et al., unpublished)
48 Cost of calcification What is the cost of calcification? (Pennington & Strathmann 1990)
49 Pluteus 7d (control) ph days 7d pluteus + 3d decalcification 3d pluteus
50
51 Calcification = >10% of energy budget
52 Cost of re-calcification 9.66% extra respiration to build a full new skeleton Cost of calcification < 10 % (control conditions) Under low ph conditions: 22% 2x increased cost to build/keep skeleton
53 Dissolution
54 Swimming - Delay in development - Morphological changes - Maintained swimming performance at a given time
55 Mechanistic understanding Growing [Dorey et al. 2013] Settle [Dorey et al. In prep] Surviving [Dorey et al. 2013; Dupont et al. 2012] Swimming [Chan et al. In prep] Calcifying [Dupont et al. In prep] Respirating [Dorey et al. 2013] Feeding [Stumpp et al. 2013] Acid-base regulation [Stumpp et al. 2012]
56 Shift in energy budget
57 Now measured
58 Shift in energy budget Physiological tipping point Increased pco
59 It s all about energy High CO 2 High CO 2
60 It s all about energy High CO 2
61 Testing the hypothesis ph Grow slower at low ph (tip point ph<7.3) Dorey et al. (2013) Glob. Change Biol.
62 E.g. organism response to multiple drivers 1. Good data on local variability / future scenarios 2. Good understanding of biological response for each driver [mode of action] 3. Build models Mix all the ingredients & test using scenarios [field, laboratory]
63 DEB modeling
64 DEB modeling
65 Good models Model + physiology = prediction on field impacts [also allow to add ecological interactions]
66 Essentially, all models are wrong, but some are useful George E. P. Box
67 E.g. organism response to multiple drivers 1. Good data on local variability / future scenarios 2. Good understanding of biological response for each driver [mode of action] 3. Build models Mix all the ingredients & test using scenarios [field, laboratory]
68 KOSMOS rocks 6 months / 50 researchers 10 x 55m 3 2 treatments: ctl vs low ph
69 Into the wild
70 Into the wild d1 d9 d17 d25 Same mortality Delay in development Desperate larvae d32
71 Field validation Predicted [mesocosm chemistry + Dorey et al. 2013] Observed
72 Into the wild 1. Good data on local variability / future scenarios 2. Good understanding of biological response for each driver [mechanisms ecology, evolution, physiology] 3. Build models Mix all the ingredients & test using scenarios [field, laboratory] It works!!!
73 Good models POLCOMS-ERSEM Surface ph Surface ph (A1B scenario) (Holt et al BG; Artioli et al JMS)
74 And act!
75 How to design your experiment 1. What is your question? Your hypothesis? 2. How can I test this? - What are my limitations? - What is the best model? - What are the best endpoints? - What are the best design/stats? - What are my controls? - etc. Can I REALLY answer my question with the collected data?
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