Transcript Slide 1

Principles of Ecology
(LSU BIOL 4253, Sections 1 & 2, Spring 2015)
Composite satellite image (“Blue Marble 2012”) from Wikimedia Commons
Dr. Kyle E. Harms
A312 Life Sciences Bldg.
[email protected]
http://www.kharms.biology.lsu.edu
K. Harms photo
The Web of Life
Complex Causation of Amphibian Deformities & Declines
Cain, Bowman & Hacker (2014), Fig. 1.13
What is Ecology?
Ernst Haeckel
German scientist, philosopher, physician
“oekologie” – combined Greek words for “household” & “knowledge”
Photo of Haeckel from Wikimedia Commons
What is Ecology?
The scientific study of interactions among
organisms and their environments
K. Harms photo
Ecology is a Component of Environmental Science
Environmental Science – interdisciplinary field that draws
concepts, expertise, and tools from natural and social sciences
Map of seasonal Gulf Coast hypoxia – the “dead zone” – from Wikimedia Commons
Ecology Can Inform Environmentalism
Environmental Movement – "a political and ethical movement that seeks to
improve and protect the quality of the natural environment through changes to
environmentally harmful human activities"
Rachel
Carson
Quote – Encyclopedia Britannica Online; photos of Carson and her 1962 book – Wikimedia Commons
Levels of Biological Organization
Principal realm
of Ecology
Image from Wikimedia Commons
50+ Years of Personal Ecological Research
(Rocky inter-tidal, coral reefs, tropical forests, etc.)
Joseph H. Connell
Photo of Connell courtesy of Pete Green
Ecological Patterns
Observations: Barnacle Inter-tidal Zonation
Semibalanus – Larger barnacle, lower in intertidal
Chthamalus – Smaller barnacle, higher in intertidal
Why?
Cain, Bowman & Hacker (2014), Fig. 12.9
Alternative Mechanistic Hypotheses
Natural ecological & evolutionary processes that
could have produced the patterns (i.e., cause-and-effect)
Barnacle Inter-tidal Zonation
Abiotic influences –
Differential physiological tolerances
to desiccation or submersion
Biotic interactions –
Interspecific competition
Predation (e.g., Thais snails prey
on Semibalanus)
Testable Predictions
Barnacle Inter-tidal Zonation
Abiotic influences –
Move barnacles outside current zones and
performance should decline
Biotic interactions –
Remove competition and zones should shift
Remove predators and zones should shift
Selected Experimental Results
Barnacle Inter-tidal Zonation
The absence of competitors & predators produced
no change in upper distributions
For Chthamalus, removing Semibalanus increased
downslope survivorship & distribution
For Semibalanus, removing Thais increased
downslope survivorship & distribution
Scientific Advancements
Observations
Jane
Goodall
Jane Goodall and chimp
Scientific Advancements
Population size
(scaled to max. size attainable)
Observations
Models (mathematical and computer)
Per capita rate of increase
Chaotic population growth
Scientific Advancements
Observations
Models (mathematical and computer)
Controlled Experiments (e.g., laboratory, microcosm, mesocosm)
http://lishaopeng.weebly.com/aquatic-algal-microcosm-experiment.html
Scientific Advancements
Observations
Models (mathematical and computer)
Controlled Experiments (e.g., laboratory, microcosm, mesocosm)
Field Experiments
Replicated fuel-manipulation treatments in Louisiana pine savanna; photo courtesy of Jonathan Myers
Experiments
Replication (i.e., n>1)
Why?
2.0 m
1.5 m
1.0 m
2.0 m
vs.
1.5 m
1.0 m
Avoid spurious influence of uncontrolled variables!
Experiments
Random Assignment of Controls & Treatments
Why?
2.0 m
1.5 m
1.0 m
2.0 m
vs.
1.5 m
1.0 m
Avoid spurious influence of uncontrolled variables!
Experiments
Statistical Analysis
Why?
2.0 m
1.5 m
2.0 m
vs.
Average
male
1.5 m
Average
female
1.0 m
1.0 m
To objectively determine whether results match predictions!
Statistical Analysis
E.g., Chi-squared Goodness-of-Fit Test
Number of individuals
p-value – probability of obtaining a test statistic at least as extreme as
observed, assuming the null hypothesis is true
6
Observed
Expected
(Null)
4
6
6
6
6
2
0
p = 1.0
(accept null)
Statistical Analysis
E.g., Chi-squared Goodness-of-Fit Test
Number of individuals
p-value – probability of obtaining a test statistic at least as extreme as
observed, assuming the null hypothesis is true
60
Observed
Expected
(Null)
40
60
50
40
50
20
0
p < 0.05
(reject null;
support alternative
hypothesis)
Statistical Analysis
E.g., t-Test
Height
(cm)

s.e.m. =
Height (cm)
160
120
80
40
0
n
Mean =
158.83
 7.55
s.e.m.
Mean =
143.67
 7.51
s.e.m.
180
175
165
155
148
130
172
160
140
135
130
125
p = 0.19
(accept null at 5%
level of significance)
Statistical Analysis
E.g., Correlation
Length of rt. hand (mm)
Correlation coefficient (r) – varies between -1 and 1;
0 = no relationship
200
180
160
140
120
100
80
60
40
20
0
0
50
100
Height (cm)
r = 0.87
150
200
Statistical Analysis
E.g., Linear Regression
Number of rabbits caught
per month
Examines the relationship between a dependent
and an independent variable; p-value tests the slope against null slope = 0;
coefficient of determination (r2) expresses how well the data fit the model
100
90
80
70
60
50
40
30
20
10
0
0
50
100
150
Height (cm)
Slope = -0.34; p < 0.05; r2 = 0.78
200
Scale in Ecology
“It is argued that the problem of pattern and scale is the
central problem in ecology, unifying population biology
and ecosystems science, and marrying
basic and applied ecology”
S. Levin (1992)
Photo of Levin from Princeton U.
Scale in Ecology
Spatial & temporal patterns often change with the scale of measurement
E.g., species-area relationship(s)
Focus
Extent
Hubbell (2001) The Unified Neutral Theory of Biodiversity & Biogeography, Fig. 6.2
Scale in Ecology
We seek mechanistic links among patterns and processes across scales
E.g., how can we extrapolate from one scale to another
(e.g., leaf-level gas exchange and photosynthesis 
forest productivity  global climate change)?
Photos from Wikimedia Commons