Download Review for Midterm

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
Announcements
• 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)
Week 1 - Anatomy
Premaxilla
Dentary
Maxilla
Heterocercal
Protocercal
•
Tip of vertebral column turns upward
•
Extends around vertebral column
•
Epicercal: dorsal lobe larger (sturgeon)
•
•
Hypocercal: ventral lobe longer (flying fish)
Embryonic fish; hagfish
Diphycercal
Homocercal
•
Vertebral column stops short of caudal fin,
which is supported by bony rays
•
Symmetrical
•
Derived fishes
•
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
Stomach
Swim bladder
Liver
Heart
Intestine
Fat deposits
Ovary
Week 2 – Evolution and Functional
Morphology & Fish ID’s
Fish Evolution: Cladogram
Agnatha
Chondrichthyes
Sarcopterygii
Actinopterygii
Osteichthyes
Bony fish
Gnathostomata
Jaws
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
Vertebrate Planktivore
Invertebrate Planktivore
Large zooplankton
Nutrients (P and N)
How do populations change?
Nt+1 = Nt + B – D + I – E




B = births
D = deaths
I = immigration
E = emigration
Immigration
Stocking
Births
Deaths
Population
Angling
Emigration
per capita annual increase
Rate of population increase
Density independent
Density dependent
N
Logistic population growth
r0 = maximum rate of increase
K= carrying capacity
per capita annual increase
dN/dt=r0N(1-N/K)
r0
N
K
Density-independent
Ricker
Recruitment
What determines recruitment?
Beverton-Holt
spawning stock biomass (SSB)
From: Wootton (1998). Ecology of teleost fishes.
Catch per unit effort (CPUE)
• Very coarse and very common index of abundance
1
Catch= 4 fish
CPUE=4/48=0.083
Effort= 4 nets for
12 hours each= 48
net hours
2
Catch=8 fish
CPUE=8/48=0.167
Effort= 4 nets for
12 hours each= 48
net hours
We conclude population 2 is 2X
larger than population 1
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
N=population size=????
C=4
R=2
Week 4 – Age and Growth
3 ways to estimate growth in natural populations
• Length Frequency Analysis
#
Caught
30
20
10
0
10
40
70
100
130
160
190
220
•Recaptures of individually marked fish
• Back calculation from calcified structures
250
280
Age this fish:
Age this fish
Frasier-Lee
Lt= c + (LT –c)(St/ST)
Annuli (t)
EDGE
(St)
(ST)
(LT)
1 1.55255574 3.34385557
2 2.29249234 3.34385557
3 2.97038463 3.34385557
3.34385557 3.34385557
194
194
194
194
(Lt)
Growth @ Age
100.788387 100.7883874
139.291536 38.50314895
174.566164 35.27462725
194 19.43383643
Problems with back calculation
• Lee's Phenomenon
LENGTH AT AGE
Age
Yr.Class
1
2
3
4
5
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
6
166
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)
AL
Linf =
523.4
Lzero =
57.54
k=
0.081
WS
Linf =
500.6
Lzero =
28.34
k=
0.080
450
400
350
Length
300
250
AL Model
W S Mode l
200
150
100
50
0
0
5
10
Age
15
20
Week 5 – Badger Mill Creek
Week 6 – Data and writing
Order of a scientific paper
(see handout!)
1.
2.
3.
4.
5.
Title
Abstract
Introduction – set up your study
Methods – study site, data analyses
Results –analyses, reference tables and
figures here
6. Discussion – interpret results
7. Literature Cited
8. 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 Tests
Hypothesis 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.
Student’s T-Test
Tests the statistical significance of the
difference between means from two
independent samples
Null hypothesis: No difference between means.
Mottled Sculpin/m2
Analysis of Variance (ANOVA)
Tests the statistical significance of the
difference between means from two or more
independent groups
Riffle Pool Run
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
0
“Normal” Scatter
Fan Shape:
Unequal
Variance
0
Curve
(linearity)
Week 7 – Foraging and Diets
Holling’s Disc Equation
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
C.S. “Buzz” Holling
Search
Encounter
Pursuit
Capture
Handling
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.
Holling’s Observations
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
Enumerating the Diet
• The “Big 3”
1. Frequency of occurrence
2. % composition by number
3. % composition by weight
• Diet Indices