A Bestiary of Experimental and Sampling Designs

1. A Bestiary of Experimental and Sampling Designs

2. REMINDERS • The goal of experimental design is to minimize the potential “sources of confusion” (Hurlbert 1984): • Temporal (and spatial) variability • Procedure effects • Experimenter bias • Experimenter-generated variability (“random error”) • Inherent variability among experimental units • Non-demonic intrusion • “…it is the elementary principles of experimental design, not advanced or esoteric ones, which are most frequently and severely violated by ecologists.”

3. The design of an experiment • The details of: • replication, • randomizations, • and independence

4. We cannot draw blood from a stone • Even the most sophisticated analysis CANNOT rescue a poor design

5. Categorical variables • They are classified into one or more unique categories • Sex (male, female) • Trophic status (producer, herbivore, carnivore) • Habitat type (shade, sun)

6. Continuous variables • They are measured on a continuous numerical scale (real or integer values) • Size • Species richness • Habitat coverage • Population density

7. Dependent and independent variables • The assignment of dependent and independent variables implies an hypothesis of cause and effect that you are trying to test. • The dependent variable is the response variable • The independent variable is the predictor variable

8. Ordinate (vertical y-axis) Abscissa (horizontal x-axis)

9. Four classes of experimental design

10. The Analysis of Covariance (ANCOVA) • It is used when there are two independent variables, one of which is categorical and one of which is continuous (the covariate)

11. Four classes of experimental design

12. Regression designs • Single-factor regression • Multiple regression

13. Single-factor regression • Collect data on a set of independent replicates • For each replicate, measure both the predictor and the response variables. • Hypothesis: seed density (the predictor variable) is responsible for rodent density (the response variable)

14. Plots Variables

15. Single-factor regression • You assume that the predictor variable is a causal variable: changes in the value of the predictor would cause a change in the value of the response • This is very different from a study in which you would examine the correlation (statistical covariation) between two variables

16. In regression (Model I) • You are assuming that the value of the independent variable is known exactly and is not subject to measurement error

17. Assumptions and caveats • Adequate replication • Independence of the data • Ensure that the range of values sampled for the predictor variable is large enough to capture the full range of responses by the response variable • Ensure that the distribution of predictor values is approximately uniform within the sample range

20. Multiple regression • Two or more continuous predictor variables are measured for each replicate, along with the single response variable

21. Assumptions and caveats • Adequate replication • Independence of the data • Ensure that the range of values sampled for the predictor variables is large enough to capture the full range of responses by the response variable • Ensure that the distribution of predictor values is approximately uniform within the sample range

22. Multiple regression • Ideally, the different predictor variables should be independent of one another; in reality, many predictor variables are correlated (e.g., height and weight) • This collinearity makes it difficult to estimate accurately regression parameters and to tease apart how much variation in the response variable is associated with each of the predictor variables

23. Multiple regression • As always, replication becomes important as we add more predictor variables to the analysis. • In many cases it is easier to measure additional predictor variables than is to obtain additional independent variables • Avoid the temptation to measure everything that you can just because it is possible

24. Multiple regression • It is a mistake to think that a model selection algorithm can identify reliably the correct set of predictor variables

25. Four classes of experimental design

26. ANOVA designs • Analysis of Variance • Treatments: refers to the different categories of the predictor variables • Replicates: each of the observations made

27. ANOVA designs • Single-factor designs • Randomized block designs • Nested designs • Multifactor designs • Split-plot designs • Repeated measurements designs • BACI designs

28. Single-factor designs • It is one of the simplest, but most powerful, experimental designs • Can readily accommodate studies in which the number of replicates per treatment is not identical (unequal sample size)

29. Single-factor designs • In a single-factor design, each of the treatments represent variation in a single predictor variable or factor • Each value of the factor that represents a particular treatment is called a treatment level

31. Good news, bad news: • This design does not explicitly accommodate environmental heterogeneity, so we need to sample the entire array of background conditions • This means the results can potentially be generalized across all environments, but… • If the noise is much stronger than the signal of the treatments, the experiment may have low power, and the analysis may not reveal treatment differences unless there are many replicates

32. Randomized block designs • An effective way to incorporate environmental heterogeneity into a design • A block is a delineated area or time period within which environmental conditions are relatively homogeneous • Blocks can be placed randomly or systematically in the study area, but should be arranged so that the environmental conditions are more similar within blocks than between them

33. Randomized block designs • Once blocks are established, replicates will still be assigned randomly to treatments, but a single replicate from each of the treatments is assigned to each block

35. Caveats • Blocks should have enough room to accommodate a single replicate of each of the treatments, and enough spacing between replicates to ensure their independence • The blocks themselves also have to be far enough apart from each other to ensure independence of replicates among blocks

36. Randomized block designs Valid blocking Invalid blocking

37. Advantages • It can be used to control for environmental gradients and patchy habitats • It is useful when your replication is constrained by space or time • Can be adapted for a matched pair lay-out

38. Disadvantages • If the sample size is small and the block effect weak, the randomized block design is less powerful than the simple one-way layout • If blocks are too small, you may introduce non-independence by physically crowding the treatments together (e.g., nectar-removal and control plots on p. 152 of Gotelli & Ellison) • If any of the replicates are lost, the data from the block cannot be used unless the missing values can be estimated indirectly

39. Disadvantages • It assumes that there is no interaction between the blocks and the treatments • BUT, replication within blocks will indeed tease apart main effects, block effects, and the interaction between blocks and treatments. It will also address the problem of missing data from within a block

40. Nested designs • It is any design in which there is subsampling within each of the replicates • In this design the subsamples are not independent of one another • The rational of this design is to increase the precision with which we estimate the response of each replicate

42. Advantages • Subsampling increases the precision of the estimate for each replicate in the design • Allows to test two hypothesis: • First: Is there variation among treatments? • Second: Is there variation among replicates within treatments? • Can be extended to a hierarchical sampling design

43. Disadvantages • They are often analyzed incorrectly • It is difficult or even impossible to analyze properly if the sample sizes are not equal • It often represents a case of misplaced sampling effort Subsampling is not a solution to inadequate replication

44. Randomized block designs • Strictly speaking, the randomized block and the nested ANOVA are two-factor designs, but the second factor (i.e., the blocks or subsamples) is included only to control for sampling variation and is not of primary interest

45. Multifactor designs • the main effects are the additive effects of each level of one treatment average over all levels of the other treatment • the interaction effects represent unique responses to particular treatment combinations that cannot be predicted simply from knowing the main effects.

46. Multifactor designs • In a multifactor design, the treatments cover two (or more) different factors, and each factor is applied in combination in different treatments. • In a multifactor design, there are different levels of the treatment for each factor

47. Multifactor designs • Why not just run two separate experiments? • Efficiency. It is often more cost effective to run a single experiment than to run two separate experiments • A multifactor design allows you to test for both main effects and for interaction effects

48. Interactions 60 50 40 West 30 North 20 10 0 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

49. Orthogonal • The key element of a proper multifactorial design is that the treatments are fully crossed or orthogonal : every treatment level of the first factor must be represented with every treatment level of the second factor • If some of the treatment combinations are missing we end with a confounded design

50. Two-factor design