Seminar by G.A. Wright Stat 601 Spring 2002
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Transcript of Seminar by G.A. Wright Stat 601 Spring 2002
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Seminar by G.A. WrightStat 601 Spring 2002
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While flying slowly in a patch of flowers, a bee may encounter an inflorescence every 0.14 s (Chittka et al., 1999)
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How do bees recognize floral perfumes among different flowers?
What characteristics of a floral perfume do they remember?
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Characteristics of floral perfumes:
- often, made up of many (100 +) odor compounds
- some compounds are present at high concentrations, others are present at low concentrations (sometimes several orders of magnitude difference)
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Robertson et al., 1993, Phytochemical Analysis
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How do floral perfumes vary among individual flowers?
- temporal variation: diurnally and developmentally
- inter-plant variation: individuals, varieties, species, and families
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ug
MTP, 1 flower, 24h
myrcene acetop ocimene me benz dimethtolunene
cismc transmc
n = 3cold=1hot=2
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P.Pink, 1 flower, 24h
myrcene acetop ocimene me benz dimethtolunene
cismc transmc
n = 17cold=3hot=14
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P.White, 1 flower, 24h
myrcene acetop ocimene me benz dimethtolunene
cismc transmc
n = 15cold=10hot=5
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Pale hybrid, 1 flower,24h
myrcene acetop ocimene me benz dimethtolunene
cismc transmc
n = 34cold=0hot=34
Variation in major scent components of snapdragon varieties
N. Dudareva and N. Gorenstein, Purdue Univ.
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Three parameters of a floral perfume that may affect the learning and memory of honeybees:
1) Types of compounds present2) Variation in the intensity of the components3) Intensity of each component relative to the intensity of the perfume
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Two types of 3-component mixtures: 1. “Similar” compounds:
hexanol, heptanol, and octanol2. “Dissimilar” compounds:
hexanol, geraniol, and octanone
Two concentrations: 1. Low: 0.0002 M2. High: 2.0 M
Methods
A 3-component mixture where one odor concentration is fixed and the others are allowed to vary randomly at low or high conc. produces 22 = 4 possible mixture combinations.
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Three Experiments
1) Constancy of a single odor component2) Average concentration of each component versus variation in individual components3) Variability of all components versus mixture osmolality
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Mixture 1
0
1
2
3
4
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odor A odor B odor C
Con
cent
ratio
n
Mixture 2
0
1
2
3
4
5
odor A odor B odor C
Con
cent
ratio
n
Mixture 3
0
1
2
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odor A odor B odor C
Con
cent
ratio
n
Mixture 4
0
1
2
3
4
5
odor A odor B odor C
Con
cent
ratio
n
Constant odor at the low concentration
Two concentrations used to make odor mixtures: low and high
Experiment I
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Mixture 1
0
1
2
3
4
5
odor A odor B odor C
Con
cent
ratio
nMixture 2
0
1
2
3
4
5
odor A odor B odor C
Con
cent
ratio
n
Mixture 3
0
1
2
3
4
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odor A odor B odor C
Con
cent
ratio
n
Mixture 4
0
1
2
3
4
5
odor A odor B odor C
Con
cent
ratio
n
Constant odor at the high concentration
Two concentrations used to make odor mixtures: low and high
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Using either the similar odors or the dissimilar odors, each component of the mixture was systematically held
constant
Bees were trained over 16 trials with either:
- constant odor at low or - constant odor at high
eg. dissimilar mixture: hexanol = constant odor
Then, they were tested with each odor component of the mixture
at either:- low concentration or - high concentration
eg. dissimilar mixture: tested with hexanol, geraniol, and octanone
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http://iris.biosci.ohio-state.edu/honeybee
Proboscis extension by honeybees during associative conditioning
Trial 1 Trial 2 Trial 3Odor
SucroseProboscis extension
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Similarlow
high
lowhigh
lowhigh
test concentrationconstant odormixture type
Dissimilarlow
high
lowhigh
lowhigh
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Used PROC LOGISTIC in SAS for analysis of data:Variables entered in the analysis:1) Level of the constant odor (coded: 0,1)2) Level of the test components (coded: 0, 1)3) Identity of the test components (coded: 0, 1)4) Response variable: 0 = no response, 1 = response
The analysis was separated by mixture type (similar and dissimilar)
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Experiment I
Tested with 0.0002 M odorant
00.10.20.30.40.50.60.70.80.9
1
const od similar od
Prob
abili
ty o
f res
pond
ing
low const
hi const
Tested with 2.0 M odorant
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1
const od similar od
Prob
abili
ty o
f res
pond
ing
Similar
Parameter DF Estimate SE Chi-Square Pr > ChiSq Exp(Est)Intercept 1 0.4055 0.1757 5.3266 0.0210 1.500mixlev 1 2.0959 0.3729 31.5902 <.0001 8.133tstlev 1 -1.0655 0.2527 17.7822 <.0001 0.345mixlev*tstlev 1 -2.5753 0.4618 31.0968 <.0001 0.076
SAS Output for logistic regression
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Tested with 0.0002 M odorant
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1
const od dissim od
Prob
abili
ty o
f res
pond
ing
low const
hi const
Tested with 2.0 M odorant
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1
const od dissim od
Prob
abili
ty o
f res
pond
ing
Dissimilar
Experiment I
Parameter DF Estimate Error Chi-Square Pr > ChiSq Exp(Est)Intercept 1 1.0442 0.2625 15.8182 <.0001 2.841mixlev 1 1.1816 0.4654 6.4461 0.0111 3.259tstlev 1 -0.6369 0.3558 3.2051 0.0734 0.529tstodre 1 0.4446 0.3280 1.8373 0.1753 1.560mixlev*tstodre 1 1.0923 0.4252 6.5980 0.0102 2.981mixlev*tstlev 1 -1.8121 0.5079 12.7287 0.0004 0.163tstlev*tstodre 1 -1.3244 0.4249 9.7167 0.0018 0.266
SAS Output for logistic regression
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Conclusions of Experiment I
Similar odors:Sensory system adaptive gain control: If training (constant) odor is high and test odor is low, the response to all odors decreases, and visa versa
Dissimilar odors:Gain Control: same as for similar odorsConstant odor preferred:If training (constant) odor the same as the test odorant, then the response to constant odor increases
Interaction between: test odorant identity and odorant intensitySuggestion of an interaction between variation and intensity
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Experiment II: Average concentration of each component vs. variation in individual components
Using either the similar odors or the dissimilar odors,
Bees were trained over 16 trials with either:
- a mixture with all odorants at a constant middle (0.02 M) - or only one odor at a constant middle (0.02 M), and the others at either low or high (thus, average middle)
Then, they were tested with each odor component of the mixture
at the low concentration
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Used PROC LOGISTIC in SAS for analysis of data:Variables entered in the analysis:1) Experiment type (coded: 0,1)2) Identity of the test components (coded: 0, 1)3) Response variable: 0 = no response, 1 = response
The analysis was separated by mixture type (similar and dissimilar)
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Tested with 0.0002 M odorant
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1
hexanol similar odor
Prob
abili
ty o
f res
pond
ing
hex const
all const
Tested with 0.0002 M odorant
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1
hexanol dissimilar odor
Prob
abili
ty o
f res
pond
ing
hex const
all const
Similar Dissimilar
Experiment II
Parameter DF Estimate SE Chi-Square Pr > ChiSq Exp(Est) Intercept 1 1.3863 0.5590 6.1497 0.0131 4.000 tstodre 1 -0.7672 0.6499 1.3936 0.2378 0.464 exp 1 -1.7540 0.7075 6.1465 0.0132 0.173 tstodre*exp 1 0.6723 0.8404 0.6400 0.4237 1.959
SAS Output for logistic regression
Parameter DF Estimate SE Chi-Square Pr > ChiSq Exp(Est) Intercept 1 -0.6190 0.4688 1.7433 0.1867 0.538 exp 1 1.1045 0.6494 2.8928 0.0890 3.018 tstodre 1 0.9213 0.5675 2.6352 0.1045 2.512 exp*tstodre 1 -1.6944 0.7882 4.6218 0.0316 0.184
Similar
Dissimilar
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Conclusions of Experiment II
When tested with the low concentration components:Similar odors:If the training odorants are at a constant concentration, the response to the test odorant increases
Dissimilar odors:Constant odor preferred: If one of the odorants is constant in the mixture, the response to the constant odorant increases
Suggestion of an interaction between variation and intensity and mixture type
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Experiment III: Variability of all components versus mixture osmolality
Using either the similar odors or the dissimilar odors,
Bees were trained over 16 trials with either:
- a mixture with all odorants at a constant (0.7 M), producing a mixture with osmolality = 2.1 M - a mixture with all odorants at varying concentrations producing a mixture with osmolality = 2.0 M - a mixture with all odorants at varying concentrations producing a mixture with osmolality = 0.03 M
Then, they were tested with each odor component of the mixture
at the low concentration
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Used PROC LOGISTIC in SAS for analysis of data:Variables entered in the analysis:1) Variability (high or low) (coded: 0,1)2) Mixture osmolality (coded: 0, 1)3) Response variable: 0 = no response, 1 = response
The analysis was separated by mixture type (similar and dissimilar)
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Tested with 0.0002 M odorant
00.10.20.30.40.50.60.70.80.9
1
hexanol similar odor
Prob
abili
ty o
f res
pond
ing
2.1 M const
2.0 var
0.03 var
Tested with 0.0002 M odorant
00.10.20.30.40.50.60.70.80.9
1
hexanol dissimilar odor
Prob
abili
ty o
f res
pond
ing
2.1 M const
2.0 var
0.03 var
Similar Dissimilar
Experiment III
Parameter DF Estimate SE Chi-Square Pr > ChiSq Exp(Est) Intercept 1 1.5755 0.3950 15.9125 <.0001 4.833 mixlev 1 1.3802 0.2205 39.1896 <.0001 3.976 cv 1 -2.3977 0.3469 47.7793 <.0001 0.091 mixtype 1 -0.6612 0.2089 10.0177 0.0016 0.516
SAS Output for logistic regression
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Conclusions of Experiment IIISimilar and Dissimilar odors:
The magnitude of the response to the low concentration components is a measurable function of:
1) Variation in the concentration of the components2) Osmolality of the mixture
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Conclusions:
1) Types of compounds present affect generalization to constant components2) Variation in the intensity of the components increases generalization to the components3) The intensity of the perfume produces an adaptive gain control which affects the ability of bees to detect low level components
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Photo courtesy of NOVA
Acknowledgements: Thanks to: Brian Smith, Amanda Mosier, Beth Skinner, Cindy Ford, Joe Latshaw, Sue Cobey, Natalia Dudareva for the snapdragons and volatiles data. Funded by NIH.