1. Principles of Ecology (LSU BIOL 4253, Sections 1 & 2, Spring 2015) Composite satellite image (“Blue Marble 2012”) from Wikimedia Commons

2. A312 Life Sciences Bldg. kharms@lsu.edu Dr. Kyle E. Harms http://www.kharms.biology.lsu.edu K. Harms photo

3. Complex Causation of Amphibian Deformities & Declines The Web of Life Cain, Bowman & Hacker (2014), Fig. 1.13

4. Ernst Haeckel German scientist, philosopher, physician “oekologie” – combined Greek words for “household” & “knowledge” What is Ecology? Photo of Haeckel from Wikimedia Commons

5. The scientific study of interactions among organisms and their environments What is Ecology? K. Harms photo

6. 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

7. 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" Ecology Can Inform Environmentalism Quote – Encyclopedia Britannica Online; photos of Carson and her 1962 book – Wikimedia Commons Rachel Carson

8. Image from Wikimedia Commons Levels of Biological Organization Principal realm of Ecology

9. Joseph H. Connell 50+ Years of Personal Ecological Research (Rocky inter-tidal, coral reefs, tropical forests, etc.) Photo of Connell courtesy of Pete Green

10. Ecological Patterns Cain, Bowman & Hacker (2014), Fig. 12.9 Observations: Barnacle Inter-tidal Zonation Semibalanus – Larger barnacle, lower in intertidal Chthamalus – Smaller barnacle, higher in intertidal Why?

11. 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) Alternative Mechanistic Hypotheses Natural ecological & evolutionary processes that could have produced the patterns (i.e., cause-and-effect)

12. 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

13. 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

14. Barnacle zonation Mechanistic Explanation / Interpretation Connell (1961) Ecology, Fig. 5

15. Observations Scientific Advancements Jane Goodall and chimp Jane Goodall

16. Observations Models (mathematical and computer) Scientific Advancements Chaotic population growth Per capita rate of increase Population size (scaled to max. size attainable)

17. Observations Models (mathematical and computer) Controlled Experiments (e.g., laboratory, microcosm, mesocosm) Scientific Advancements http://lishaopeng.weebly.com/aquatic-algal-microcosm-experiment.html

18. Observations Models (mathematical and computer) Controlled Experiments (e.g., laboratory, microcosm, mesocosm) Field Experiments Scientific Advancements Replicated fuel-manipulation treatments in Louisiana pine savanna; photo courtesy of Jonathan Myers

19. Replication (i.e., n>1) Experiments Why? 1.0 m 1.5 m 2.0 m 1.0 m 1.5 m 2.0 m vs. Avoid spurious influence of uncontrolled variables!

20. Experiments Why? 1.0 m 1.5 m 1.0 m 1.5 m 2.0 m Avoid spurious influence of uncontrolled variables! vs. Random Assignment of Controls & Treatments

21. Statistical Analysis Experiments Why? To objectively determine whether results match predictions! 1.0 m 1.5 m 1.0 m 1.5 m 2.0 m vs. Average male Average female

22. E.g., Chi-squared Goodness-of-Fit Test Statistical Analysis 0 2 4 6 Number of individuals p-value – probability of obtaining a test statistic at least as extreme as observed, assuming the null hypothesis is true 6 6 Observed Expected (Null) 6 6 p = 1.0 (accept null)

23. E.g., Chi-squared Goodness-of-Fit Test Statistical Analysis p-value – probability of obtaining a test statistic at least as extreme as observed, assuming the null hypothesis is true 0 20 40 60 Number of individuals 60 40 Observed Expected (Null) 50 p < 0.05 (reject null; support alternative hypothesis)

24. Height (cm) E.g., t-Test Statistical Analysis 0 40 120 160 Height (cm) 180 175 172 160 p = 0.19 (accept null at 5% level of significance) 165 140 155 135 148 130 125 80 Mean = 158.83  7.55 s.e.m. Mean = 143.67  7.51 s.e.m. s.e.m. =   n n

25. E.g., Correlation Statistical Analysis Correlation coefficient (r) – varies between -1 and 1; 0 = no relationship r = 0.87 Height (cm) Length of rt. hand (mm)

26. E.g., Linear Regression Statistical Analysis Slope = -0.34; p < 0.05; r 2 = 0.78 Examines the relationship between a dependent and an independent variable; p-value tests the slope against null slope = 0; coefficient of determination (r 2 ) expresses how well the data fit the model Height (cm) Number of rabbits caught per month

27. Photo of Levin from Princeton U. “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) Scale in Ecology

28. E.g., species-area relationship(s) Focus Extent Hubbell (2001) The Unified Neutral Theory of Biodiversity & Biogeography, Fig. 6.2 Spatial & temporal patterns often change with the scale of measurement Scale in Ecology

29. Photos from Wikimedia Commons E.g., how can we extrapolate from one scale to another (e.g., leaf-level gas exchange and photosynthesis  forest productivity  global climate change)? We seek mechanistic links among patterns and processes across scales Scale in Ecology