Post on 01-Jan-2016
Review for Midterm
Zoo511 - 2011
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
Week 1 - Anatomy
Maxilla
Premaxilla
Dentary
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
Spines• Rigid• Never segmented• Often for defense
Rays• Flexible• Often branched• Mainly for support
Fisheries ecologists use both spines & rays for identification and aging!
Basic Mouth Types
Superior
Terminal
Sub-Terminal Inferior
Scale types• Ganoid
• Placoid
• Cycloid
• Ctenoid
Swim bladder
Ovary
Heart
Liver
Stomach
IntestineFat deposits
Week 2 – Evolution and Functional Morphology & Fish ID’s
Jaws
Osteichthyes
Gnathostomata
Bony fish
ActinopterygiiSarcopterygiiChondrichthyesAgnatha
Fish Evolution: Cladogram
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
Body Types
Jaw Shapes
Practice
Practice
Practice
Practice
Practice
Week 3 – Population Dynamics
Nutrients (P and N)
Large zooplankton
Invertebrate PlanktivoreVertebrate Planktivore
Nt+1 = Nt + B – D + I – E
B = births D = deaths I = immigration E = emigration
How do populations change?
DeathsPopulationBirths
Emigration
Immigration
Stocking
Angling
Rate of population increase
Density independent
Density dependent
per
cap
ita a
nn
ual in
crease
N
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
What determines recruitment?
spawning stock biomass (SSB)
Ricker
Beverton-Holt
Density-independent
From: Wootton (1998). Ecology of teleost fishes.
Rec
ruit
men
t
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
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
Mark recapture
M=5 C=4 R=2
N=population size=????
Week 4 – Age and Growth
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
Age this fish:
Age this fish
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)
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
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)
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
Week 5 – Badger Mill Creek
Week 6 – Data and writing
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
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).
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
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.
Tests the statistical significance of the difference between means from two independent samples
Student’s T-Test
Null hypothesis: No difference between means.
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.
Simple Linear Regression
• Analyzes relationship between two continuous variables: predictor and response
•Null hypothesis: there is no relationship (slope=0)
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!
Residual Plots Can Help Test Assumptions
0
“Normal” Scatter
0Fan Shape: Unequal Variance
0
Curve (linearity)
Week 7 – Foraging and Diets
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
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
Enumerating the Diet
• The “Big 3”1. Frequency of occurrence2. % composition by number3. % composition by weight
• Diet Indices