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The method of synthesis in ecology
Author(s) -
Ford E. David,
Ishii Hiroaki
Publication year - 2001
Publication title -
oikos
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.672
H-Index - 179
eISSN - 1600-0706
pISSN - 0030-1299
DOI - 10.1034/j.1600-0706.2001.930117.x
Subject(s) - construct (python library) , consistency (knowledge bases) , causal inference , coherence (philosophical gambling strategy) , computer science , epistemology , function (biology) , management science , criticism , inference , scientific modelling , ecology , data science , artificial intelligence , mathematics , econometrics , art , philosophy , statistics , literature , evolutionary biology , economics , biology , programming language
Synthesis of results from different investigations is an important activity for ecologists but when compared with analysis the method of synthesis has received little attention. Ecologists usually proceed intuitively and this can lead to a problem in defining differences between the syntheses made by different scientists. It also leads to criticism from scientists favoring analytical approaches that the construction of general theory is an activity that does not follow the scientific method.
We outline a methodology for scientific inference about integrative concepts and the syntheses made in constructing them and illustrate how this can be applied in the development of general theory from investigations into particular ecological systems. The objective is to construct a causal scientific explanation. This has four characteristics. (1) It defines causal and/or organizational processes that describe how systems function. (2) These processes are consistent – under the same conditions they will produce the same effect. (3) A causal scientific explanation provides general information about events of a similar kind. (4) When experiments are possible then a designed manipulation will produce a predictable response.
The essential characteristic of making synthesis to construct a causal scientific explanation is that it is progressive and we judge progress made by assessing the coherence of the explanation using six criteria: acceptability of individual propositions including that they have been tested with data, consistency of concept definitions, consistency in the type of concepts used in making the explanation, that ad hoc propositions are not used, that there is economy in the number of propositions used, that the explanation applies to broad questions.
We illustrate development of a causal scientific explanation for the concept of long‐lived pioneer tree species, show how the coherence of this explanation can be assessed, and how it could be improved.