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