Research Methods in Behavioural Ecology

1. Research Methods in Behavioural Ecology

2. Hypotheses, models, predictions, & theories • Principles are logical construction • Hypotheses are too, but they’re more tentative • Model: A formalized representation of a system • Can be ‘hypothetical’ • Mathematical, computational, physical • Prediction: A logical outcome of a model or hypothesis that can be tested against data • Studies: Attempts to disprove hypotheses • Theory: A broadly general body of scientific understanding

3. Correlation and Causation • What is correlation? • Height and traffic tickets • Phones and longevity • Confounding variables • Age, economic development • Drawing time

4. The logic of experiments • Manipulation of independent variable • Random assignment • Control groups • Solves the confound problem • Drawing time • Barn swallows

5. Natural Experiments • “Nature” changes independent variable of interests • E.g., disasters, human interference • Harpy eagle calls and howler monkeys From Gil da Costa et al. 2002 http://www.dkimages.com/discover/previews/1453/11181132.JPG

6. The Phenotypic Approach • The most common approach • “…we assume that studies at the level of the phenotype are sufficient for identifying the selective pressures that exert themselves on the organism…”

7. Phenotypic: Optimization I • Optimality models • Assumption: Behavior serves to maximize some currency of fitness • Energy intake, access to eggs, etc. • Economic logic • Behavioral choices incur fitness costs and benefits; find maximum benefit – cost • Allow us to predict what animals should do if they are perfectly adapted to their environments

8. Phenotypic: Optimization II • But… • Tradeoffs and constraints limit ‘perfectability’ • Our models should recognize that optimization is maximization under constraints • Proximately, animals behave to achieve “objective function” • Match of “cost function” to “objective function” • What happens when the environment changes? • An example of optimality thinking

9. Phenotypic:Game Theory • Employed when the cost function is affected by the strategy played by others in the population • E..g, Which side to drive on, the sex ratio game • All players make adaptive choice • Population level vs. individual fitness • The evolutionarily stable strategy (ESS) cannot be invaded by one or a small group of actors playing an alternative strategy http://www.abc.net.au/reslib/200707/r162700_598492.jpg

10. The Hawk-Dove Game • Resource value = V • Two strategies: Hawk & Dove • Hawks attack everyone • 50% chance of winning against hawk, 100% against dove • Cost of losing a fight = L • Doves display to doves, flee from hawks • 50% chance of winning against dove, 0% against hawk, but no cost of fighting • Cost of display = D

11. The Hawk-Dove Game • Suppose V = 50, L = 100, D = 10 • With these parameters, it’s good to be the rare phenotype • A “Mixed ESS” • This logic can explain the persistence of behavioral variability • If V > L, hawk is usually a pure ESS

12. Proxies of Fitness • The phenotypic approach assumes equivalent demographic & genotypic fitness • Proxies of demographic fitness • Do these actually relate to fitness? • E.g, in the case of chicks in the nest, is there assurance of paternity?

13. Phenotypic engineering • The logic of engineered “mutation” • Ablation experiments • Muting Scott’s seaside sparrows • Can show current stabilizing, neutral, or directional selection • How? • What about selection in the past? • Why might a trait be expressed at a suboptimal level?

14. Limits to the Phenotypic Approach • Assumption of ‘state of adaptedness’ • What if environments are rapidly changing? • Assumption of single locus, haploid inheritance • What about heterozygote advantage? • Could coexistence of three “malaria” types be explained from phenotypic observations? • Assumption of unconstrained adaptation • http://www.youtube.com/watch?v=enrLSfxTqZ0&feature=related

15. The Genetic Approach • Monogenic determinism of behavior is rare • for gene in fruit flies • Polygenic determinism is predominant • Meshing gene / phenotype relationships to defined traits • Variable expression of complex behavioral traits is attributable to variation in genes, the environment, and epigenetic factors Sokolowski 2001, Nat Rev.

16. Quantitative genetics of behavior • Comparing populations • Common garden experiments • Controlling for simple maternal effects • Frightened fish • Transplant experiments • Looking for maternal effects • Crossing experiments Brown et al. 2007 BES

17. Quantitative genetics of behavior II • Artificial selection • Continuous traits • Generally, diversifying selection • What do these kinds of experiments tell us? • What can be learned from cloning? Knockouts?

18. The Comparative Approach • Comparing traits among species • Without phylogeny • Relate behavior to ecological factors across spp. or populations • E.g., Eggshell removal • Problems • Causation? • “Cherry picking” examples that support hypotheses • Confounds, like size, phylogeny

19. Allometry • Body size is an important confound in comparative studies • Scaling one body part against another is tricky • Allometry is the study of the relationship between body measurements • log(Y)= b log (X) + log (a) • Slope (b) > 1 means Y increases faster than X • “positive allometry” • Comparing residuals is informative

20. Controlling for phylogeny • Phylogenetic inertia • Homoplasy and homology • Determining ancestral characters • Maximum parsimony • Problem of equal parsimony

21. Method of Independent Contrasts • Looks for relationship between two continuous variables while controlling for phylogeny • Examples • Assumes random change, independent changes in different branches

22. Is evolution of x correlated with evolution of y (and if so, how)?

23. Method of Maximum Likelihood • Discrete variables • E.g., duetting and monogamy • 1st model: State changes in two variables are independent (a) • 2nd model: State changes are interdependent (b) • Can find most likely direction, order