Seminar by G.A. Wright Stat 601 Spring 2002. While flying slowly in a patch of flowers, a bee may...

transcript

Seminar by G.A. WrightStat 601 Spring 2002

While flying slowly in a patch of flowers, a bee may encounter an inflorescence every 0.14 s (Chittka et al., 1999)

How do bees recognize floral perfumes among different flowers?

What characteristics of a floral perfume do they remember?

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)

Robertson et al., 1993, Phytochemical Analysis

How do floral perfumes vary among individual flowers?

- temporal variation: diurnally and developmentally

- inter-plant variation: individuals, varieties, species, and families

1 2 3 4 5 6 7

MTP, 1 flower, 24h

myrcene acetop ocimene me benz dimethtolunene

cismc transmc

n = 3cold=1hot=2

1 2 3 4 5 6 7

P.Pink, 1 flower, 24h

cismc transmc

n = 17cold=3hot=14

1 2 3 4 5 6 7

P.White, 1 flower, 24h

cismc transmc

n = 15cold=10hot=5

1 2 3 4 5 6 7

Pale hybrid, 1 flower,24h

cismc transmc

n = 34cold=0hot=34

Variation in major scent components of snapdragon varieties

N. Dudareva and N. Gorenstein, Purdue Univ.

Three parameters of a floral perfume that may affect the learning and memory of honeybees:

1) Types of compounds present

2) Variation in the intensity of the components

3) Intensity of each component relative to the intensity of the perfume

Two types of 3-component mixtures:

1. “Similar” compounds: hexanol, heptanol, and octanol

2. “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.

Three Experiments

1) Constancy of a single odor component

2) Average concentration of each component versus variation in individual components

3) Variability of all components versus mixture osmolality

Mixture 1

odor A odor B odor C

Mixture 2

Mixture 3

Mixture 4

Constant odor at the low concentration

Two concentrations used to make odor mixtures: low and high

Experiment I

Mixture 1

nMixture 2

Mixture 3

Mixture 4

Constant odor at the high concentration

Two concentrations used to make odor mixtures: low and high

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

http://iris.biosci.ohio-state.edu/honeybee

Proboscis extension by honeybees during associative conditioning

Trial 1 Trial 2 Trial 3Odor

Sucrose

Proboscis extension

Similarlow

lowhigh

test concentrationconstant odormixture type

Dissimilarlow

lowhigh

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)

Experiment I

Tested with 0.0002 M odorant

00.10.20.30.40.50.60.70.80.9

const od similar od

low const

hi const

00.10.20.30.40.50.60.70.80.9

const od similar od

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

00.10.20.30.40.50.60.70.80.9

const od dissim od

low const

hi const

00.10.20.30.40.50.60.70.80.9

const od dissim od

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

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

Experiment II: Average concentration of each component vs. variation in individual components

Using either the similar odors or the dissimilar odors,

- 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)

at the low concentration

1) Experiment type (coded: 0,1)

2) Identity of the test components (coded: 0, 1)

00.10.20.30.40.50.60.70.80.9

hexanol similar odor

hex const

all const

00.10.20.30.40.50.60.70.80.9

hexanol dissimilar odor

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

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

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

Experiment III: Variability of all components versus mixture osmolality

Using either the similar odors or the dissimilar odors,

- 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

at the low concentration

1) Variability (high or low) (coded: 0,1)

2) Mixture osmolality (coded: 0, 1)

00.10.20.30.40.50.60.70.80.9

hexanol similar odor

2.1 M const

2.0 var

0.03 var

00.10.20.30.40.50.60.70.80.9

hexanol dissimilar odor

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

Conclusions of Experiment III

Similar 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

Conclusions:

1) Types of compounds present affect generalization to constant components

2) Variation in the intensity of the components increases generalization to the components

3) The intensity of the perfume produces an adaptive gain control which affects the ability of bees to detect low level components

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.

Seminar by G.A. Wright Stat 601 Spring 2002. While flying slowly in a patch of flowers, a bee may...

Documents