ECOLOGY AND SCIENCE
I. SCIENTIFIC INVESTIGATIONS
“…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)
A. Knowledge, Prediction, Understanding
Learning is “the act, process, or experience of gaining knowledge”
1. Knowledge
"the set of ideas that agree with or are consistent with the facts of nature" (Romesburg 1981: 293)
On Truth and Facts (Guthery 2004)
Facts of history/observation – I caught an 18” cutthroat in the St. Joe at 1012 on 24 June 2004.
Fact of measurement – The average size of cutthroat caught on the St. Joe is 12”.
Fact of pattern – I generally catch trout larger than the average fly fisher.
Fact of conjecture – I am truly an excellent angler
** Key point is that facts may be fuzzy
2. Prediction and Understanding
In science, we continually adapt and refine current ideas to anticipate and predict novel events (i.e., facts of nature). Along the way we discard stagnant or degenerating ideas for better ones. Through this process we begin to understand the relationships between certain factors and certain outcomes (i.e., causes).
D. Philosophy/Methods of Science
1. Popperian Method of Science
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
4) Devise critical test and repeatedly test hypotheses looking for falsification
5) Retain unfalsified hypothesis. If >1 unfalsified hypotheses, retain the one with greatest degree of corroboration
· Degree of corroboration ~ Number of tests that do not falsify hypothesis + empirical content of hypothesis
Empirical content ~ the “length” of the list of possibilities that would falsify the hypothesis
**Note: This basically ends Popper’s method of science. However, he also briefly stated that if one 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. Hypothetico-deductive Method of Science
1) Observations and literature review
2) Conceptual model
3) Formulate “research hypothesis”
4) Take opposite and set as “statistical/null” hypothesis
5) Test null hypothesis with critical experiment
6) Evaluate and interpret test
7) Speculate new hypotheses
3. Lakatos’ Method of Science
"All theories...are born refuted and die refuted. But are they equally good?" (Lakatos 1978)
1) Same as 1) through 3) of Popper
4) 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.
E. Modeling in Fish and Wildlife Ecology
1. What is a model?
Example: Simple models of population growth
a) Exponential
b) Logistic
c) Modeling Terms
· Variable
Response -
Predictor -
· Parameter
· Parameter Estimate
· Likelihood
2. A Modeling Approach
Generally follows Lakatos’ method of science (i.e., keep best available model).
Use selection criteria for determining best available model
a) Kullback-Leibler distance
b) Bias vs. Variance
c) Akaike’s information criteria (AIC)
AIC is an estimate of the expected Kullback-Leibler distance between the approximating model and truth
d) AIC from Sum of Squares Error (i.e., Residual Sum of Squares; RSS)
**Note: For linear regression with normally distributed errors, the number of parameters (K) is the number of variables + 2 (e.g., intercept and variance)**
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
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.
Updated 31 July 1996