ECOLOGY AND SCIENCE
I. SCIENTIFIC INVESTIGATIONS
Goal of Science: Prediction and understanding
“…the aim of science [is] to find satisfactory explanations of whatever strikes us as being in need of explanation” (Popper 1972:191)
“Prediction and explanation are the twin pillars upon which the scientific enterprise rests.” (Casti and Karlqvist 1991:vii)
1. Karl Popper
“because all theories remain] guesses, conjectures, hypotheses… we are therefore at best always faced with the question of preferring, tentatively, some guesses to others…” (Popper 1972:13)
1) Begin with a problem based on observations that generate new questions or contradict established theory
2) Develop conceptual hypothesis/model
3) Formulate specific hypotheses/models (several of them) that make testable predictions
4) Devise critical test and repeatedly test hypotheses looking for falsification (evidence contrary to model predictions)
5) Retain unfalsified hypothesis. If >1 unfalsified hypothesis, retain the one with greatest degree of corroboration
**Note: This basically ends Popper’s method of science. However, he also briefly stated that if you could falsify all but one of the total possible competing theories you would have retained the TRUTH. This is the basis for hypothetico-deductive method and Platt's (1964) "strong inference"
2. Imre Lakatos
"All theories...are born refuted and die refuted. But are they equally good?" (Lakatos 1978)
· Same as 1) through 3) of Popper
· Keep best available hypothesis
**Do not have to retain only unfalsified hypotheses because of the philosophy that hypotheses may never be truly falsified AND science may keep a hypothesis that is wrong if there is not a better one available.
'Science and Pseudoscience'B. Methods of Science (see Hobbs and Hilborn 2006)
**Platt's Strong Inference
Competition between a single hypothesis and the data. Somewhat follows Popper BUT often leads to hypotheses with very little empirical content (especially when we do the little trick for null hypothesis testing where we “test” the opposite of what we think it true)
**Information theoretic
Competition between multiple hypotheses (models) and the data. Follows Lakatos’ philosophy
C. Modeling in Fish and Wildlife Ecology
To manage fish and wildlife based on science, we need to predict... to predict we need to model.
1. What is a model?
An abstraction that symbolizes the operation of processes in
nature. It is no more than a
hypothesis clearly stated.
a. Modeling Terms
Model Variables
**Response variable (Y): what we are interested in predicting
Observed data: y1, y2, y3, …yn
**Predictor variable (X): any auxiliary information we could use to predict Y
Observed data: x1, x2, x3, …xn
Model Structure
**Probability function: Function
(equation) that calculates the probability that a random draw (event) from a discrete variate Y (e.g.,
number of offspring, number of dots on a die) will take the value of y.
f(y) = Pr{Y = y}
**Probability density function (pdf): For a continuous variate Y (length, mass, population density) the area under the pdf (equation) between 2 points yL, yU is the probability that a random draw (event) y will lie between yL and yU
**Parameter(s): Specifies the mathematical relationships within the model including those between the predictor variable(s) and the response
**Example of a Model...
2. Parameter Estimatation
Maximum Likelihood (R. A. Fisher 1922): We can use our observed data to find the most likely parameter values for a particular model...
What are we doing? Trying to find the values of the parameters that maximize the probability of observing what we observed. The value of the parameter(s) that make the likelihood as large as possible.
**Example of Maximum Likelihood...
3. Model Selection
To get good predictions (remember this is our goal!), we need a model that will closely approximate reality
How can we measure how close our model is to reality (i.e., truth)?
Metric for measuring distance
Estimates of K-L Distance
A. Why is writing so important?
1. Integral part of all methods of science
2. To convey current state of knowledge
3. To allow for a challenge
B. How to write scientifically
Scientific Writing ppt.
III. COURSE PROJECT
Purpose: To provide students with an opportunity to improve skills in study design, data collection and analysis, interpretation of results, and scientific writing.
Instructions for Course Project
Key References
Burnham, K. P. and D. R. Anderson. 1998. Model selection and inference: a practical information-theoretic approach. Springer-Verlag, New York, New York, USA.
Casti, J. L. and A. Karlqvist. 1991. Beyond Belief. CRC Press, Boca Raton, Florida, USA.
Guthery, F. S. 2004. Commentary: the flavors and colors of facts in wildlife science. Wildlife Society Bulletin 32:288-297.
Hilborn, R. and M. Mangel. 1997. The ecological detective. Princeton University Press, Princeton, New Jersey, USA.
Lakatos, I. 1978. The methodology of scientific research programmes. Cambridge University Press, New York, New York, USA.
Platt, J. R. 1964. Strong inference. Science 146:347-352.
Popper, K. R. 1972. Objective knowledge. Oxford University Press, Oxford, England.
Romesburg, H. C. 1981. Wildlife science: gaining reliable knowledge. Journal of Wildlife Management 45:293-313.
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