Bio-Diversity Lecture PPt
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Transcript of Bio-Diversity Lecture PPt
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Species diversity:rarefaction, evenness and indices
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Are these two communities equally diverse?
Site 1 Site 2
Sp A 12 49
Sp B 0 1
Sp C 0 1
Sp D 0 1
Problem 1: Accounting for sample size
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Are these two communities equally diverse?
Site 1 Site 2
Sp A 12 49
Sp B 11 1
Sp C 14 1
Sp D 13 1
Problem 2: Accounting for abundance
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Resampling techniques
• Can be used to estimate a statistic or parameter for a different sample size
• Can be used to estimate a statistic or parameter under the null hypothesis of no treatment effect
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Smartie diversity!
• Suppose we found 5 colours represented in a sample of 10 smarties. Is this the same diversity (species richness) as in your sample?
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Phyc 2003 Practice data setFr
eque
ncy
R
6
88
243
232
189
109
58
35
1910 9
1
-0.05-0.10-0.15-0.20 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40
0.48
.477
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Phyc 2003 Practice data setFr
eque
ncy
R
6
88
243
232
189
109
58
35
1910 9
1
-0.05-0.10-0.15-0.20 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40
0.48
.477
1999
P= = 0.001
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Are these two communities equally diverse?
Site 1 Site 2
Sp A 12 49
Sp B 11 1
Sp C 14 1
Sp D 13 1
Problem 2: Accounting for abundance
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3 Ways to include abundance in species diversity
1. Plot the distribution of individuals amongst species.
2. Summarize both abundance and species richness in a single index.
3. Examine the evenness of the distribution of individuals amongst species
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Log-series distribution
Log abundance class per species
Num
ber
of s
peci
es
“Most species are rare”
Most species are represented by only a couple of individuals (i.e. rare). Only a few highly-abundant (i.e. common) species.
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Log-normal
Most species are do NOT have abundances of only a few individuals, but rather have intermediate abundances (on a log scale! Still low)
Log abundance class per species
Num
ber
of s
peci
es
Log normal
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2003 class mite data
Log2 abundance class
Ess
entia
lly n
umbe
r of
spe
cies
PRIMER “Geometric class plot”
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2. Summarize everything in one index
• Simpson’s (1-lambda, or 1-D in Krebs)
• Shannon-Wiener
• Alpha (a parameter from log series)
• Margalef d
Some indices output by PRIMER (formulas in Krebs and Magurran)
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Site 1 Site 2 Site 1
-p*lnp
Site 2
-p*lnp
Sp A 12 49 0.34 0.06
Sp B 11 1 0.33 0.08
Sp C 14 1 0.36 0.08
Sp D 13 1 0.35 0.08
sum 50 52 1.38 0.28
Shannon-Wiener =
sum (-Proportion spA * ln (prop spA)+
(-Proportion spB*ln(prop spB)...)
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Which index?
Read Krebs and Magurran and consider:
- Sensitivity to differences in sample size.
- Do you want differences in rare or abundant species to be emphasized?
- Do you want differences in species richness or evenness to be emphasized?
- How does log-normal vs. log-series affect?
- Performance in other studies (what works?).
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3. Measure evenness separately
Pielou’s J: Comparison of actual Shannon-Wiener with Shannon-Wiener if species had equal proportion (log S).
Question: Why would Shannon-Wiener = logS if species equally abundant?
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3. Measure evenness separately
Pielou’s J:
J = Shannon-Wiener / logS
• Close to 1: very even distribution of abundances amongst species
• Close to 0: very uneven
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Site 1 Site 2 Site 1
-p*lnp
Site 2
-p*lnp
Sp A 12 49 0.34 0.06
Sp B 11 1 0.33 0.08
Sp C 14 1 0.36 0.08
Sp D 13 1 0.35 0.08
Sum (Shannon-Wiener) 1.38 0.28
Log S 1.39 1.39
J’ 1.00 0.20