How good must models and data be in ecology?

Linear programming models of diet selection (LP) have been criticized as being too sensitive to variations in parameter values that have not been or may not be able to be measured with a high degree of precision (small standard error). Therefore, LP's predictions have been questioned, even thou...

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Bibliographic Details
Published in:Oecologia 1994-12, Vol.100 (4), p.475-480
Main Author: Belovsky, G.E. (Utah State Univ., Logan (USA). Dept. of Fisheries and Wildlife. Center of Ecology)
Format: Article
Language:English
Subjects:
ANALISIS ESTADISTICO
ANALYSE STATISTIQUE
Animal, plant and microbial ecology
Biological and medical sciences
BUSQUEDA DE ALIMENTO
Diet
ECOLOGIA
Ecological modeling
ECOLOGIE
Foraging
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Grasses
Herbivores
Linear Programming
Methods and techniques (sampling, tagging, trapping, modelling...)
MODELE DE SIMULATION
Modeling
MODELOS DE SIMULACION
Monte Carlo methods
Optimal foraging
Parametric models
Philosophy
PROGRAMACION LINEAL
PROGRAMMATION LINEAIRE
RECHERCHE DE NOURRITURE
Sensitivity analysis
Short Communications
Statistical discrepancies
Statistics
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Summary:Linear programming models of diet selection (LP) have been criticized as being too sensitive to variations in parameter values that have not been or may not be able to be measured with a high degree of precision (small standard error). Therefore, LP's predictions have been questioned, even though the predicted diet choices agree very well with observations in 400 published tests. The philosophical and statistical aspects of this criticism of LP are reviewed in light of the ability to test any nontrivial ecological theory. It is argued that measures of error in field data may not meet simple statistical definitions, and thereby, may make sensitivity analyses that use the error measures overly conservative. Furthermore, the important issue in testing ecological theory may not be the statistical confidence in a single test, but whether or not the theory withstands repeated tests
ISSN:0029-8549
1432-1939
DOI:10.1007/BF00317870