1. Review for Midterm Zoo511 - 2011

2. Plan for today Go over Hypotheses/Questions Quick review of key concepts from each lecture via powerpoint slides – These are central ideas to most of the lectures, but there will be questions from slides that are not included today, so don’t just study based on today’s review! – These are simply slides from previous lectures, so no new material Question/Answer – You’ll get to review more material if you actually ask questions Hypotheses/Questions: Graded and emailed back to you with comments on the documents (note about reach length data) Midterm right after spring break – be ready! – Test format Start working on your rough drafts! – 1 st draft due in class Week 10 (March 29 or 30) Announcements

3. Week 1 - Anatomy

4. Maxilla Premaxilla Dentary

5. Heterocercal Tip of vertebral column turns upward Epicercal: dorsal lobe larger (sturgeon) Hypocercal: ventral lobe longer (flying fish) Protocercal Extends around vertebral column Embryonic fish; hagfish Homocercal Vertebral column stops short of caudal fin, which is supported by bony rays Symmetrical Derived fishes Diphycercal 3 lobed; lungfish and coelacanth Vertebral column extends to end of caudal fin, dividing into symmetrical parts

6. Spines Rigid Never segmented Often for defense Rays Flexible Often branched Mainly for support Fisheries ecologists use both spines & rays for identification and aging!

7. Basic Mouth Types Superior Terminal Sub-Terminal Inferior

8. Scale types Ganoid Placoid Cycloid Ctenoid

9. Swim bladder Ovary Heart Liver Stomach Intestine Fat deposits

10. Week 2 – Evolution and Functional Morphology & Fish ID’s

11. Jaws Osteichthyes Gnathostomata Bony fish ActinopterygiiSarcopterygiiChondrichthyesAgnatha Fish Evolution: Cladogram

12. Major Trends in Fish Evolution Changes in cranium and jaw structure – Branchiostegal rays – Pre-maxilla separation Changes in movement – Loss of external armor – Fins – Air bladders

13. Body Types

14. Jaw Shapes

15. Practice

20. Week 3 – Population Dynamics

21. Nutrients (P and N) Large zooplankton Invertebrate Planktivore Vertebrate Planktivore

22. N t+1 = N t + B – D + I – E  B = births  D = deaths  I = immigration  E = emigration How do populations change? Deaths Population Births Emigration Immigration Stocking Angling

23. Rate of population increase Density independent Density dependent per capita annual increase N

24. Logistic population growth K= carrying capacity r 0 = maximum rate of increase dN/dt=r 0 N(1-N/K ) per capita annual increase N K r0r0

25. What determines recruitment? spawning stock biomass (SSB) Ricker Beverton-Holt Density-independent From: Wootton (1998). Ecology of teleost fishes. Recruitment

26. Catch per unit effort (CPUE) Very coarse and very common index of abundance Effort= 4 nets for 12 hours each= 48 net hours Catch= 4 fish CPUE=4/48=0.083 Effort= 4 nets for 12 hours each= 48 net hours Catch=8 fish CPUE=8/48=0.167 We conclude population 2 is 2X larger than population 1 1 2

27. Population abundance Density estimates (#/area) – Eggs estimated with quadrats – Pelagic larvae sampled with modified plankton nets – Juvenile and adult fish with nets, traps, hook and line, or electrofishing Density is then used as index of abundance, or multiplied by habitat area to get abundance estimate

28. Mark recapture M=5 C=4 R=2 N=population size=????

29. Week 4 – Age and Growth

30. 3 ways to estimate growth in natural populations Length Frequency Analysis Recaptures of individually marked fish Back calculation from calcified structures # Caught

31. Age this fish:

32. Age this fish

33. Frasier-Lee L t = c + (L T –c)(S t /S T )

34. Problems with back calculation Lee's Phenomenon AgeYr.Class123456 1198890 2198990115 3199080112139 4199175108133150 519926696129147160 619935992126147156166 LENGTH AT AGE

35. Von Bertalanffy Growth Equation L t = L ∞ - (L ∞ - L 0 ) exp (-kt) – L t = length at time 't’ – L ∞ = length at infinity – L 0 = length at time zero (birth) – K = constant ( shape of growth line)

37. L t = L ∞ - (L ∞ - L 0 ) exp (-kt) Linf =523.4 Lzero =57.54 k =0.081 Linf =500.6 Lzero =28.34 k =0.080 AL WS

38. Week 5 – Badger Mill Creek

39. Week 6 – Data and writing

40. Order of a scientific paper (see handout!) 1.Title 2.Abstract 3.Introduction – set up your study 4.Methods – study site, data analyses 5.Results –analyses, reference tables and figures here 6.Discussion – interpret results 7.Literature Cited 8.Tables and figures

41. Note on results Make ecology the subject of your sentences, not statistics. Statistics help you tell your story, they are not your story in themselves. WRONG: Linear regression showed that there was a significant positive relationship with a p-value of 0.04 and an R 2 of 0.81 between brown trout abundance and flow velocity. RIGHT: Brown trout abundance increased with increasing flow velocity (R 2 =0.81, p=0.04).

42. Peer Review Criticism is important…”constructive criticism” is best! Two types: Internal and External. Point of internal review is to make external review go well Reviews need to be taken seriously

43. Statistical Tests Hypothesis Testing: In statistics, we are always testing a Null Hypothesis (H o ) against an alternate hypothesis (H a ). p-value: The probability of observing our data or more extreme data assuming the null hypothesis is correct Statistical Significance: We reject the null hypothesis if the p-value is below a set value (α), usually 0.05.

44. Tests the statistical significance of the difference between means from two independent samples Student’s T-Test Null hypothesis: No difference between means.

45. Analysis of Variance (ANOVA) Tests the statistical significance of the difference between means from two or more independent groups Riffle Pool Run Mottled Sculpin/m 2 Null hypothesis: No difference between means.

46. Simple Linear Regression Analyzes relationship between two continuous variables: predictor and response Null hypothesis: there is no relationship (slope=0)

47. P-value: probability of observing your data (or more extreme data) if no relationship existed. Indicates the strength of the relationship, you can think of this as a measure of predictability R-Squared indicates how much variance in the response variable is explained by the explanatory variable. If this is low, other variables likely play a role. If this is high, it DOES NOT INDICATE A SIGNIFICANT RELATIONSHIP!

48. Residual Plots Can Help Test Assumptions 0 “Normal” Scatter 0 Fan Shape: Unequal Variance 0 Curve (linearity)

49. Week 7 – Foraging and Diets

50. Holling’s Disc Equation C.S. “Buzz” Holling Holling, C. S. 1959. The components of predation as revealed by a study of small mammal predation of the European pine sawfly. Canadian Entomologist 91:293–320. Rate of Energy Gained = (λe – s)/(1 +λh) λ = rate of encounter with diet item e = energy gained per encounter s = cost of search per unit time h = average handling time Search Encounter Pursuit Capture Handling

51. Predation rates ↑ with ↑ prey densities happens due to 2 effects: 1.Functional response by predator -Type 1 -Type 2 -Type 3 2.Numerical response by predator -Reproduction -Aggregation Holling’s Observations

52. Enumerating the Diet The “Big 3” 1. Frequency of occurrence 2. % composition by number 3. % composition by weight Diet Indices