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Transcript of Review for Midterm Zoo511 - 2011. Plan for today Go over Hypotheses/Questions Quick review of key...
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Review for Midterm
Zoo511 - 2011
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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!– 1st draft due in class Week 10 (March 29 or 30)
Announcements
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Week 1 - Anatomy
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Maxilla
Premaxilla
Dentary
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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
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Spines• Rigid• Never segmented• Often for defense
Rays• Flexible• Often branched• Mainly for support
Fisheries ecologists use both spines & rays for identification and aging!
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Basic Mouth Types
Superior
Terminal
Sub-Terminal Inferior
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Scale types• Ganoid
• Placoid
• Cycloid
• Ctenoid
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Swim bladder
Ovary
Heart
Liver
Stomach
IntestineFat deposits
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Week 2 – Evolution and Functional Morphology & Fish ID’s
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Jaws
Osteichthyes
Gnathostomata
Bony fish
ActinopterygiiSarcopterygiiChondrichthyesAgnatha
Fish Evolution: Cladogram
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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
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Body Types
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Jaw Shapes
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Practice
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Practice
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Practice
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Practice
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Practice
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Week 3 – Population Dynamics
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Nutrients (P and N)
Large zooplankton
Invertebrate PlanktivoreVertebrate Planktivore
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Nt+1 = Nt + B – D + I – E
B = births D = deaths I = immigration E = emigration
How do populations change?
DeathsPopulationBirths
Emigration
Immigration
Stocking
Angling
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Rate of population increase
Density independent
Density dependent
per
cap
ita a
nn
ual in
crease
N
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Logistic population growth
K= carrying capacityr0 = maximum rate of increase
dN/dt=r0N(1-N/K)
per
cap
ita a
nn
ual
incr
ease
NK
r0
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What determines recruitment?
spawning stock biomass (SSB)
Ricker
Beverton-Holt
Density-independent
From: Wootton (1998). Ecology of teleost fishes.
Rec
ruit
men
t
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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
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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
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Mark recapture
M=5 C=4 R=2
N=population size=????
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Week 4 – Age and Growth
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3 ways to estimate growth in natural populations• Length Frequency Analysis
•Recaptures of individually marked fish
• Back calculation from calcified structures
#C
augh
t
0
10
20
30
10 40 70 100 130 160 190 220 250 280
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Age this fish:
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Age this fish
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Annuli (t) (St) (ST) (LT) (Lt) Growth @ Age1 1.55255574 3.34385557 194 100.788387 100.78838742 2.29249234 3.34385557 194 139.291536 38.503148953 2.97038463 3.34385557 194 174.566164 35.27462725
EDGE 3.34385557 3.34385557 194 194 19.43383643
Frasier-Lee Lt= c + (LT –c)(St/ST)
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Problems with back calculation
• Lee's Phenomenon
Age Yr.Class 1 2 3 4 5 6
1 1988 90
2 1989 90 115
3 1990 80 112 139
4 1991 75 108 133 150
5 1992 66 96 129 147 160
6 1993 59 92 126 147 156 166
LENGTH AT AGE
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Von Bertalanffy Growth Equation
• Lt = L∞ - (L∞ - L0) exp (-kt)
– Lt = length at time 't’
– L∞ = length at infinity
– L0 = length at time zero (birth)
– K = constant ( shape of growth line)
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Lt = L∞ - (L∞ - L0) exp (-kt)
0
50
100
150
200
250
300
350
400
450
0 5 10 15 20
Age
Length AL Model
WS Model
Linf = 523.4
Lzero = 57.54
k = 0.081
Linf = 500.6
Lzero = 28.34
k = 0.080
AL WS
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Week 5 – Badger Mill Creek
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Week 6 – Data and writing
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Order of a scientific paper (see handout!)
1. Title2. Abstract3. Introduction – set up your study4. Methods – study site, data analyses5. Results –analyses, reference tables
and figures here6. Discussion – interpret results7. Literature Cited8. Tables and figures
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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 R2 of 0.81 between brown trout abundance and flow velocity.
RIGHT: Brown trout abundance increased with increasing flow velocity (R2=0.81, p=0.04).
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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
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Statistical TestsHypothesis Testing: In statistics, we are always testing a Null Hypothesis (Ho) against an alternate hypothesis (Ha).
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.
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Tests the statistical significance of the difference between means from two independent samples
Student’s T-Test
Null hypothesis: No difference between means.
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Analysis of Variance (ANOVA)Tests the statistical significance of the difference between means from two or more independent groups
Riffle Pool Run
Mott
led
Scul
pin/
m2
Null hypothesis: No difference between means.
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Simple Linear Regression
• Analyzes relationship between two continuous variables: predictor and response
•Null hypothesis: there is no relationship (slope=0)
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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!
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Residual Plots Can Help Test Assumptions
0
“Normal” Scatter
0Fan Shape: Unequal Variance
0
Curve (linearity)
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Week 7 – Foraging and Diets
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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 iteme = energy gained per encounters = cost of search per unit timeh = average handling timeSearch
EncounterPursuitCaptureHandling
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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
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Enumerating the Diet
• The “Big 3”1. Frequency of occurrence2. % composition by number3. % composition by weight
• Diet Indices