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How To Talk Good
Robert Williamson
Ecology & Evolutionary Biology
What goes in a talk?
What are common problems with talks?
What should go on a slide?
How can I make my slides clearer?
Section1–Whatgoesinatalk?
Section1–Whatgoesinatalk?
What makes a talk good?
(Or not so good?)
Section1–Whatgoesinatalk?
Methods
Results
Discussion
Introduction
Methods
Results
Discussion
Introduction
Methods
Results
Discussion
Introduction
Methods
Results
Discussion
Introduction
Section1–Whatgoesinatalk?
Section2–Whatarecommonproblemswithtalks?
What are slides for?
Section2–Whatarecommonproblemswithtalks?
What are slides for?
What are they NOT for?
Section2–Whatarecommonproblemswithtalks?
Section2–Whatarecommonproblemswithtalks?
“PowerPoint Is Evil. Power Corrupts.
PowerPoint Corrupts Absolutely.”
– Edward Tufte
“PowerPoint may now and then benefit the
bottom 10% of all presenters. PP forces
the really inept to have points, some
points, any points.”
– Edward Tufte
Review of Test Data Indicates Conservatism for Tile
Penetration
•The existing SOFI on tile test data used to create Crater was
reviewed along with STS-87 Southwest Research data
- Crater overpredicted penetration of tile coating
significantly
 Initial penetration to described by normal velocity
• Varies with volume/mass of projectile (e.g., 100ft/sec
for 3cu. In)
 Significant energy is required for the softer SOFI
particle to penetrate the relatively hard tile coating
• Test results do show that it is possible at sufficient
mass and velocity
 Conversely, once tile is penetrated SOFI can cause
significant damage
• Minor variations in total energy (above penetration
level) can cause significant tile damage
- Flight condition is significantly outside of test
database
 Volume of ramp is 1920cu in vs 3 cu in for test
The Cognitive Style of PowerPoint. E. Tufte
Section2–Whatarecommonproblemswithtalks?
Section2–Whatarecommonproblemswithtalks?
Section2–Whatarecommonproblemswithtalks?
What are some ways that slides
are often misused?
SENSI NG CI RCUI T ARCH I TECH TURE
• Parallel
• Series
Life History Traits
Life history traits - Physiological and morphological characteristics
• Variation in characteristics for organisms can alter fitness
• Phenology (i.e. timing of expression)
• Effort (e.g. fecundity)
• Allocation of Energy
Trade-offs between traits
Modified by environment
• How does maternal competition influence life history traits?
Section2–Whatarecommonproblemswithtalks?
In 15 minutes or less...
Motivation & aims;!
Tools & methods;!
Preliminary results;!
Future directions & implications.
Section2–Whatarecommonproblemswithtalks?
Outline
I will give a talk on some stuff
Section2–Whatarecommonproblemswithtalks?
!! 5!
Section3–Whatshouldgoonaslide?
Section3–Whatshouldgoonaslide?
How can we use slides better?
Write a sentence headline that states the
main assertion of the slide
In the body of the slide, support the headline
assertion with visual evidence, using text only
when necessary
Modified from Garner. Intl J Eng. 2013
The&genome&of&the&Norwegian&spruce&
is&100×&the&size&of&A.#thaliana&
Section3–Whatshouldgoonaslide?
Section3–Whatshouldgoonaslide?
Is there variation in transposon copy
number among Capsella species?
TE copy
number
Ågren et al. In review
Is there variation in transposon copy
number among Capsella species?
TE copy
number
Ågren et al. In review
Is there variation in transposon copy
number among Capsella species?
TE copy
number
Ågren et al. In review
Is there variation in transposon copy
number among Capsella species?
TE copy
number
Ågren et al. In review
Section3–Whatshouldgoonaslide?
Temporal variation leads to
two predictions for overall
ratchet rate
Temporal
Variation
Selection Coefficient (s)
RatchetRate
Temporal variation leads to
two predictions for overall
ratchet rate
Temporal
Variation
Selection Coefficient (s)
RatchetRate
0
Temporal variation leads to
two predictions for overall
ratchet rate
Temporal
Variation
Selection Coefficient (s)
RatchetRate
smax0
Temporal variation leads to
two predictions for overall
ratchet rate
Temporal
Variation
Selection Coefficient (s)
RatchetRate
smax0
Rs_min = 0
Temporal variation leads to
two predictions for overall
ratchet rate
Temporal
Variation
Selection Coefficient (s)
RatchetRate
smax0
Rs_max
Rs_min = 0
Temporal variation leads to
two predictions for overall
ratchet rate
Temporal
Variation
Selection Coefficient (s)
RatchetRate
smax0 savg
Rs_max
Rs_avg
Rs_min = 0
Temporal variation leads to
two predictions for overall
ratchet rate
Temporal
Variation
Selection Coefficient (s)
RatchetRate
smax0 savg
Rs_max
Average of Rs_min = 0 and Rs_max
Rrate_avg
Rs_min = 0
Temporal variation leads to
two predictions for overall
ratchet rate
Temporal
Variation
Selection Coefficient (s)
RatchetRate
smax0 savg
Rs_max
Rs_avg
Rrate_avg
Rs_min = 0
Section3–Whatshouldgoonaslide?
Section3–Whatshouldgoonaslide?
Temporal variation can
increase ratchet rate
Conclusion
Ratchet rate increases with temporal
autocorrelation
Rate of fitness decline can increase
indefinitely with s
Ratchet rate and fitness decline in a
divided genome can be predicted by:
Temporal autocorrelation
increases ratchet rate
Results
RatchetRate
Selection Coefficient (s)
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.05 0.1 0.15 0.2 0.250 0.05 0.10 0.15 0.20 0.25
Temporal variation can
increase ratchet rate
Conclusion
Ratchet rate increases with temporal
autocorrelation
Rate of fitness decline can increase
indefinitely with s
Ratchet rate and fitness decline in a
divided genome can be predicted by:
R = F*Rfluctuating + (1 - F)*Rconstant
Temporal variation can
increase ratchet rate
Conclusion
Ratchet rate increases with temporal
autocorrelation
Rate of fitness decline can increase
indefinitely with s
Ratchet rate and fitness decline in a
divided genome can be predicted by:
Section4–HowcanImakemyslidesclearer?
Section4–HowcanImakemyslidesclearer?
Kelly 1965 Color Engineering
Green-Armytage 2010 Colour: Design & Creativity
http://tinyurl.com/GoodColors
Is there variation in transposon copy
number among Capsella species?
TE copy
number
Ågren et al. In review
Section4–HowcanImakemyslidesclearer?
Section4–HowcanImakemyslidesclearer?
vonHoldt 2009 Nature
Section4–HowcanImakemyslidesclearer?
vonHoldt 2009 Nature
http://tinyurl.com/CBsim
Section4–HowcanImakemyslidesclearer?
vonHoldt 2009 Nature
Section4–HowcanImakemyslidesclearer?
http://tinyurl.com/GoodColors
Section4–HowcanImakemyslidesclearer?
http://tinyurl.com/GoodColors
Section4–HowcanImakemyslidesclearer?
Section4–HowcanImakemyslidesclearer?
Dunn 2001 Evolution
Lekking species have the most dimorphism in
plumage.
Section4–HowcanImakemyslidesclearer?
Section4–HowcanImakemyslidesclearer?
http://tinyurl.com/Pdigit
Section4–HowcanImakemyslidesclearer?
http://tinyurl.com/Pdigit
Section4–HowcanImakemyslidesclearer?
http://tinyurl.com/Pdigit
Section4–HowcanImakemyslidesclearer?
http://tinyurl.com/Pdigit
Section4–HowcanImakemyslidesclearer?
http://tinyurl.com/Pdigit
Section4–HowcanImakemyslidesclearer?
http://tinyurl.com/Pdigit
Section4–HowcanImakemyslidesclearer?
0
1
2
3
4
5
6
7
Mating System
Dimorphism
Adapted from Dunn 2001 Evolution
Lekking species have the most dimorphism in
plumage.
Section4–HowcanImakemyslidesclearer?
Section4–HowcanImakemyslidesclearer? The Ne of Pinus species has a strong correlation
with the amount of neutrality in their genomes.
0.0
0.1
0.2
0.3
0.4
0.5
log(Ne) (thousands)
Proportionof
effectivelyneutralaminoacidsites
Helianthus annuus
Helianthus petiolaris
Capsella grandiflora
Helianthus argophyllus
Helianthus tuberosus
Helianthus exilis
Sorghum bicolor
Zea mays
Populus tremula
Oryza rufipogon
Arabidopsis lyrata
Arabidopsis thaliana
Medicago trunculata Expresed
Boechera stricta
Schiedea globosa
Helianthus paradoxus
Populus balsamifera
Medicago trunculata Not Expressed
Pinus spp.
10 20 40 80 100 200 400 800
Hough et al. 2012 AREES
Section4–HowcanImakemyslidesclearer?
Section4–HowcanImakemyslidesclearer?
0.2
0.22
0.24
0.26
0.28
0.3
0 10 20 30 40
Neutrality
Log(Ne) - thousands
The Ne of Pinus species has a strong correlation
with the amount of neutrality in their genomes.
Adapted from Hough et al. 2012 AREES
Talks that I cannibalized
Anu Mehta “Quorum Sensing” http://f1000.com/posters/browse/summary/1095431
Alison Wardlaw et al. “Muller’s Ratchet Temporal Variation in Selection Accelerates Mutational Decay.” Presented at the Atwood Colloquium UofT EEB 2012.
Anne Land “Tracking the flu: motivation and understanding of a community of flu-reporters.” http://f1000.com/posters/browse/summary/1097622
Arvid Ågren. “Mating System Shifts and Transposable Element Evolution.” Presented at the Atwood Colloquium UofT EEB 2014.
B R Erick Peirson et al. “Collaborative networks & conceptual change: a computational study of 20th-century genecology in Britain.”
http://f1000.com/posters/browse/summary/1097254
Matthew A Kaproth et al. “Maternal effects of plant competition: comparing introduced populations to their native counterparts.”
http://f1000.com/posters/browse/summary/1094185
Rachel Adams “Shrooms in rooms: what fungi in the built environment can tell us about fungi and buildings.’
http://f1000.com/posters/browse/summary/1096778 Sarah Erhart et al. “Investigating the enacted and stated curriculum in physical chemistry”
Presented at the ACS National Meeting 2015
David L. Stern - http://www.howtogiveatalk.com/how-to-give-a-talk.html
Edward Tufte - http://www.edwardtufte.com/tufte/index
Assertion Evidence Guide - http://writing.engr.psu.edu/speaking.html
Green-Armytage P. 2010 “A Colour Alphabet and the Limits of Colour Coding.”
Colour: Design & Creativity 5(10)1-23.
Kelly. 1965 “Twenty-Two Colors of Maximum Contrast.” Color Engineering .
http://tinyurl.com/GoodColors
http://tinyurl.com/Pdigit
Other useful stuff

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Presentation lecture w3_2015_v2