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The experimental paradigm and long‐term population studies
Author(s) -
KREBS CHARLES J.
Publication year - 1991
Publication title -
ibis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.933
H-Index - 80
eISSN - 1474-919X
pISSN - 0019-1019
DOI - 10.1111/j.1474-919x.1991.tb07663.x
Subject(s) - term (time) , ecology , population , scale (ratio) , temporal scales , argument (complex analysis) , spatial ecology , environmental resource management , geography , biology , environmental science , sociology , biochemistry , physics , demography , cartography , quantum mechanics
Most ecologists recognize the value of long‐term studies to population and community ecology, and many also subscribe to the experimental approach as the most effective way of obtaining ecological knowledge. But if we are experimentalists, do we need long‐term studies? I argue that the answer to this question is yes, that we must combine these two approaches to solve the major ecological questions of the next century. Most of the challenging questions facing ecologists involve systems subject to long‐term time trends or high environmental variability. Because of the statistical power of many ecological methods, long‐term studies are essential to measure time trends in ecosystems. Ignoring statistical power has been a major problem with short‐term studies, which have predominated in the ecological literature. Some examples of long‐term studies on larch bud‐moth Zeiraphera diniana , winter moth Operophthera brumata and snowshoe hares are discussed briefly to illustrate the four major desiderata of long‐term projects: spatial scale, sampling design, hypothesis testing and timeframe. Two reasons for not doing long‐term studies are to assess density‐dependence and to monitor ecosystem health. The density‐dependent paradigm is bankrupt and has produced much argument and little understanding of population processes. Monitoring of populations is politically attractive but ecologically banal unless it is coupled with experimental work to understand the mechanisms behind system changes.

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